Systems and Methods to Provide Real-Time Offers via a Cooperative Database

ABSTRACT

In one aspect, a computing apparatus is configured to: store transaction data recording transactions processed by a transaction handler; organize third party data according to community, where the third party data includes first data received from a first plurality of entities of a first community and second data received from a second plurality of entities of a second community; and responsive to a request from a merchant in the second community, present an offer of the merchant in the second community to users identified via the transaction data and the first data received from the first plurality of entities of the first community. In one embodiment, the first data provides permission from the merchant in the first community to allow the merchant in the second community to use intelligence information of the first community to identify users for targeting offers from the merchant in the second community.

RELATED APPLICATIONS

The present application claims priority to U.S. Pat. App. Ser. No.61/380,098, filed Sep. 3, 2010 and entitled “Systems and Methods toProvide Real-Time Offers via a Cooperative Database,” the disclosure ofwhich application is hereby incorporated herein by reference.

FIELD OF THE TECHNOLOGY

At least some embodiments of the present disclosure relate to theprocessing of transactions, such as payments made via credit cards,debit cards, prepaid cards, etc., and/or providing information based onthe processing of the transaction data.

BACKGROUND

Millions of transactions occur daily through the use of payment cards,such as credit cards, debit cards, prepaid cards, etc. Correspondingrecords of the transactions are recorded in databases for settlement andfinancial recordkeeping (e.g., to meet the requirements of governmentregulations). Such data can be mined and analyzed for trends,statistics, and other analyses. Sometimes such data are mined forspecific advertising goals, such as to provide targeted offers toaccount holders, as described in PCT Pub. No. WO 2008/067543 A2,published on Jun. 5, 2008 and entitled “Techniques for Targeted Offers.”

U.S. Pat. App. Pub. No. 2009/0216579, published on Aug. 27, 2009 andentitled “Tracking Online Advertising using Payment Services,” disclosesa system in which a payment service identifies the activity of a userusing a payment card as corresponding with an offer associated with anonline advertisement presented to the user.

U.S. Pat. No. 6,298,330, issued on Oct. 2, 2001 and entitled“Communicating with a Computer Based on the Offline Purchase History ofa Particular Consumer,” discloses a system in which a targetedadvertisement is delivered to a computer in response to receiving anidentifier, such as a cookie, corresponding to the computer.

U.S. Pat. No. 7,035,855, issued on Apr. 25, 2006 and entitled “Processand System for Integrating Information from Disparate Databases forPurposes of Predicting Consumer Behavior,” discloses a system in whichconsumer transactional information is used for predicting consumerbehavior.

U.S. Pat. No. 6,505,168, issued on Jan. 7, 2003 and entitled “System andMethod for Gathering and Standardizing Customer Purchase Information forTarget Marketing,” discloses a system in which categories andsub-categories are used to organize purchasing information by creditcards, debit cards, checks and the like. The customer purchaseinformation is used to generate customer preference information formaking targeted offers.

U.S. Pat. No. 7,444,658, issued on Oct. 28, 2008 and entitled “Methodand System to Perform Content Targeting,” discloses a system in whichadvertisements are selected to be sent to users based on a userclassification performed using credit card purchasing data.

U.S. Pat. App. Pub. No. 2005/0055275, published on Mar. 10, 2005 andentitled “System and Method for Analyzing Marketing Efforts,” disclosesa system that evaluates the cause and effect of advertising andmarketing programs using card transaction data.

U.S. Pat. App. Pub. No. 2008/0217397, published on Sep. 11, 2008 andentitled “Real-Time Awards Determinations,” discloses a system forfacilitating transactions with real-time awards determinations for acardholder, in which the award may be provided to the cardholder as acredit on the cardholder's statement.

The disclosures of the above discussed patent documents are herebyincorporated herein by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 illustrates a system to provide services based on transactiondata according to one embodiment.

FIG. 2 illustrates the generation of an aggregated spending profileaccording to one embodiment.

FIG. 3 shows a method to generate an aggregated spending profileaccording to one embodiment.

FIG. 4 shows a system to provide information based on transaction dataaccording to one embodiment.

FIG. 5 illustrates a transaction terminal according to one embodiment.

FIG. 6 illustrates an account identifying device according to oneembodiment.

FIG. 7 illustrates a data processing system according to one embodiment.

FIG. 8 shows the structure of account data for providing loyaltyprograms according to one embodiment.

FIG. 9 shows a system to provide real-time messages according to oneembodiment.

FIG. 10 shows a method to provide real-time messages according to oneembodiment.

FIG. 11 shows a system to arrange the delivery of real-time messagesaccording to one embodiment.

FIG. 12 shows a method to arrange the delivery of real-time messagesaccording to one embodiment.

FIG. 13 illustrates a graphical representation of triggers according toone embodiment.

FIG. 14 illustrates a method to present triggers according to oneembodiment.

FIG. 15 illustrates a system to use a cooperative database to targetoffers according to one embodiment.

FIG. 16 illustrates a method to target offers using a cooperativedatabase according to one embodiment.

DETAILED DESCRIPTION Introduction

In one embodiment, transaction data, such as records of transactionsmade via credit accounts, debit accounts, prepaid accounts, bankaccounts, stored value accounts and the like, is processed to provideinformation for various services, such as reporting, benchmarking,advertising, content or offer selection, customization, personalization,prioritization, etc.

In one embodiment, an advertising network is provided based on atransaction handler to present personalized or targetedadvertisements/offers on behalf of advertisers. A computing apparatusof, or associated with, the transaction handler uses the transactiondata and/or other data, such as account data, merchant data, searchdata, social networking data, web data, etc., to develop intelligenceinformation about individual customers, or certain types or groups ofcustomers. The intelligence information can be used to select, identify,generate, adjust, prioritize, and/or personalize advertisements/offersto the customers. In one embodiment, the transaction handler is furtherautomated to process the advertisement fees charged to the advertisers,using the accounts of the advertisers, in response to the advertisingactivities.

In one embodiment, the computing apparatus is to generate triggerrecords for a transaction handler to identify authorization requeststhat satisfy the conditions specified in the trigger records, identifycommunication references of the users associated with the identifiedauthorization requests, and use the communication references to targetreal-time messages at the users in parallel with the transaction handlerproviding responses to the respective authorization requests. Details inone embodiment regarding the generation and delivery of messages inreal-time with the processing of transactions are provided in thesection entitled “REAL-TIME MESSAGES.”

In one embodiment, the computing apparatus is to receive an inputspecifying a set of users, identify a set of triggers (each of whichcorresponds to a set of one or more conditions which when satisfiedcause transmission of a message to the set of users), rank the triggersbased at least in part on the transaction data of the users that wasrecorded by the transaction handler, and generate a ranked list of thetriggers based on the ranking. In one embodiment, the computingapparatus is to select one or more triggers from the ranked list for amarketer. In another embodiment, the ranked list is presented to amarketer for selection. Details in one embodiment regarding the rankingand selecting of triggers for the generation and delivery of real-timemessages are provided in the section entitled “TRIGGER RANK.”

In one embodiment, the computing apparatus is to identify a set ofusers, identify a plurality of triggers (each of which corresponds to aset of one or more conditions which when satisfied by a transaction of auser from the set, as the transaction is being processed by thetransaction handler, cause transmission of an advertisement to theuser), provide a graphical representation of triggers, and provide datarelated to the triggers on the graphical representation, in response touser interaction with the graphical representation. In one embodiment,the graphical representation includes a map showing locations of thetriggers, the location of desired transactions to be caused by theadvertisement, and indication of ranks of the triggers ranked using thetransaction data of the transaction handler. Details in one embodimentregarding the visualization of triggers for the generation and deliveryof real-time messages are provided in the section entitled “TRIGGERVISUALIZATION.”

In one embodiment, the computing apparatus is configured with acooperative database that organizes data and/or participants accordingto communities to allow merchants to deliver offers via differentmerchants or advertisers. In one embodiment, the computing apparatusallows a merchant to target cardholders based on a different communityor participating list. For example, through the use of the cooperativedatabase, merchant A can utilize merchant B's list to identifycardholders who have not shopped at merchant A and then send a targetedoffer to the identified cardholders. Thus, the cooperative databaseallows merchants to identify customer acquisition opportunities to sendoffers to cardholders who have not shopped at or have not recently beento the stores of the merchants. Details in one embodiment regarding theuse of a cooperative database for the generation and delivery ofreal-time messages are provided in the section entitled “COOPERATIVEDATABASE.”

In one embodiment, the computing apparatus correlates transactions withactivities that occurred outside the context of the transaction, such asonline advertisements presented to the customers that at least in partcause offline transactions. The correlation data can be used todemonstrate the success of the advertisements, and/or to improveintelligence information about how individual customers and/or varioustypes or groups of customers respond to the advertisements.

In one embodiment, the computing apparatus correlates, or providesinformation to facilitate the correlation of, transactions with onlineactivities of the customers, such as searching, web browsing, socialnetworking and consuming advertisements, with other activities, such aswatching television programs, and/or with events, such as meetings,announcements, natural disasters, accidents, news announcements, etc.

In one embodiment, the correlation results are used in predictive modelsto predict transactions and/or spending patterns based on activities orevents, to predict activities or events based on transactions orspending patterns, to provide alerts or reports, etc.

In one embodiment, a single entity operating the transaction handlerperforms various operations in the services provided based on thetransaction data. For example, in the presentation of the personalizedor targeted advertisements, the single entity may perform the operationssuch as generating the intelligence information, selecting relevantintelligence information for a given audience, selecting, identifying,adjusting, prioritizing, personalizing and/or generating advertisementsbased on selected relevant intelligence information, and facilitatingthe delivery of personalized or targeted advertisements, etc.Alternatively, the entity operating the transaction handler cooperateswith one or more other entities by providing information to theseentities to allow these entities to perform at least some of theoperations for presentation of the personalized or targetedadvertisements.

System

FIG. 1 illustrates a system to provide services based on transactiondata according to one embodiment. In FIG. 1, the system includes atransaction terminal (105) to initiate financial transactions for a user(101), a transaction handler (103) to generate transaction data (109)from processing the financial transactions of the user (101) (and thefinancial transactions of other users), a profile generator (121) togenerate transaction profiles (127) based on the transaction data (109)to provide information/intelligence about user preferences and spendingpatterns, a point of interaction (107) to provide information and/oroffers to the user (101), a user tracker (113) to generate user data(125) to identify the user (101) using the point of interaction (107), aprofile selector (129) to select a profile (131) specific to the user(101) identified by the user data (125), and an advertisement selector(133) to select, identify, generate, adjust, prioritize and/orpersonalize advertisements for presentation to the user (101) on thepoint of interaction (107) via a media controller (115).

In one embodiment, the system further includes a correlator (117) tocorrelate user specific advertisement data (119) with transactionsresulting from the user specific advertisement data (119). Thecorrelation results (123) can be used by the profile generator (121) toimprove the transaction profiles (127).

In one embodiment, the transaction profiles (127) are generated from thetransaction data (109) in a way as illustrated in FIGS. 2 and 3. Forexample, in FIG. 3, an aggregated spending profile (341) is generatedvia the factor analysis (327) and cluster analysis (329) to summarize(335) the spending patterns/behaviors reflected in the transactionrecords (301).

In one embodiment, a data warehouse (149) as illustrated in FIG. 4 iscoupled with the transaction handler (103) to store the transaction data(109) and other data, such as account data (111), transaction profiles(127) and correlation results (123). In FIG. 4, a portal (143) iscoupled with the data warehouse (149) to provide data or informationderived from the transaction data (109), in response to a query requestfrom a third party or as an alert or notification message.

In FIG. 4, the transaction handler (103) is coupled between an issuerprocessor (145) in control of a consumer account (146) and an acquirerprocessor (147) in control of a merchant account (148). An accountidentification device (141) is configured to carry the accountinformation (142) that identifies the consumer account (146) with theissuer processor (145) and provide the account information (142) to thetransaction terminal (105) of a merchant to initiate a transactionbetween the user (101) and the merchant.

FIGS. 5 and 6 illustrate examples of transaction terminals (105) andaccount identification devices (141). FIG. 7 illustrates the structureof a data processing system that can be used to implement, with more orfewer elements, at least some of the components in the system, such asthe point of interaction (107), the transaction handler (103), theportal (143), the data warehouse (149), the account identificationdevice (141), the transaction terminal (105), the user tracker (113),the profile generator (121), the profile selector (129), theadvertisement selector (133), the media controller (115), etc. Someembodiments use more or fewer components than those illustrated in FIGS.1 and 4-7, as further discussed in the section entitled “VARIATIONS.”

In one embodiment, the transaction data (109) relates to financialtransactions processed by the transaction handler (103); and the accountdata (111) relates to information about the account holders involved inthe transactions. Further data, such as merchant data that relates tothe location, business, products and/or services of the merchants thatreceive payments from account holders for their purchases, can be usedin the generation of the transaction profiles (127, 341).

In one embodiment, the financial transactions are made via an accountidentification device (141), such as financial transaction cards (e.g.,credit cards, debit cards, banking cards, etc.); the financialtransaction cards may be embodied in various devices, such as plasticcards, chips, radio frequency identification (RFID) devices, mobilephones, personal digital assistants (PDAs), etc.; and the financialtransaction cards may be represented by account identifiers (e.g.,account numbers or aliases). In one embodiment, the financialtransactions are made via directly using the account information (142),without physically presenting the account identification device (141).

Further features, modifications and details are provided in varioussections of this description.

Centralized Data Warehouse

In one embodiment, the transaction handler (103) maintains a centralizeddata warehouse (149) organized around the transaction data (109). Forexample, the centralized data warehouse (149) may include, and/orsupport the determination of, spending band distribution, transactioncount and amount, merchant categories, merchant by state, cardholdersegmentation by velocity scores, and spending within merchant target,competitive set and cross-section.

In one embodiment, the centralized data warehouse (149) providescentralized management but allows decentralized execution. For example,a third party strategic marketing analyst, statistician, marketer,promoter, business leader, etc., may access the centralized datawarehouse (149) to analyze customer and shopper data, to providefollow-up analyses of customer contributions, to develop propensitymodels for increased conversion of marketing campaigns, to developsegmentation models for marketing, etc. The centralized data warehouse(149) can be used to manage advertisement campaigns and analyze responseprofitability.

In one embodiment, the centralized data warehouse (149) includesmerchant data (e.g., data about sellers), customer/business data (e.g.,data about buyers), and transaction records (301) between sellers andbuyers over time. The centralized data warehouse (149) can be used tosupport corporate sales forecasting, fraud analysis reporting,sales/customer relationship management (CRM) business intelligence,credit risk prediction and analysis, advanced authorization reporting,merchant benchmarking, business intelligence for small business,rewards, etc.

In one embodiment, the transaction data (109) is combined with externaldata, such as surveys, benchmarks, search engine statistics,demographics, competition information, emails, etc., to flag key eventsand data values, to set customer, merchant, data or event triggers, andto drive new transactions and new customer contacts.

Transaction Profile

In FIG. 1, the profile generator (121) generates transaction profiles(127) based on the transaction data (109), the account data (111),and/or other data, such as non-transactional data, wish lists, merchantprovided information, address information, information from socialnetwork websites, information from credit bureaus, information fromsearch engines, and other examples discussed in U.S. patent applicationSer. No. 12/614,603, filed Nov. 9, 2009, published as U.S. Pat. App.Pub. No. 2011/0054981, and entitled “Analyzing Local Non-TransactionalData with Transactional Data in Predictive Models,” the disclosure ofwhich is hereby incorporated herein by reference.

In one embodiment, the transaction profiles (127) provide intelligenceinformation on the behavior, pattern, preference, propensity, tendency,frequency, trend, and budget of the user (101) in making purchases. Inone embodiment, the transaction profiles (127) include information aboutwhat the user (101) owns, such as points, miles, or other rewardscurrency, available credit, and received offers, such as coupons loadedinto the accounts of the user (101). In one embodiment, the transactionprofiles (127) include information based on past offer/coupon redemptionpatterns. In one embodiment, the transaction profiles (127) includeinformation on shopping patterns in retail stores as well as online,including frequency of shopping, amount spent in each shopping trip,distance of merchant location (retail) from the address of the accountholder(s), etc.

In one embodiment, the transaction handler (103) provides at least partof the intelligence for the prioritization, generation, selection,customization and/or adjustment of an advertisement for delivery withina transaction process involving the transaction handler (103). Forexample, the advertisement may be presented to a customer in response tothe customer making a payment via the transaction handler (103).

Some of the transaction profiles (127) are specific to the user (101),or to an account of the user (101), or to a group of users of which theuser (101) is a member, such as a household, family, company,neighborhood, city, or group identified by certain characteristicsrelated to online activities, offline purchase activities, merchantpropensity, etc.

In one embodiment, the profile generator (121) generates and updates thetransaction profiles (127) in batch mode periodically. In otherembodiments, the profile generator (121) generates the transactionprofiles (127) in real-time, or just in time, in response to a requestreceived in the portal (143) for such profiles.

In one embodiment, the transaction profiles (127) include the values fora set of parameters. Computing the values of the parameters may involvecounting transactions that meet one or more criteria, and/or building astatistically-based model in which one or more calculated values ortransformed values are put into a statistical algorithm that weightseach value to optimize its collective predictiveness for variouspredetermined purposes.

Further details and examples about the transaction profiles (127) in oneembodiment are provided in the section entitled “AGGREGATED SPENDINGPROFILE.”

Non-Transactional Data

In one embodiment, the transaction data (109) is analyzed in connectionwith non-transactional data to generate transaction profiles (127)and/or to make predictive models.

In one embodiment, transactions are correlated with non-transactionalevents, such as news, conferences, shows, announcements, market changes,natural disasters, etc. to establish cause and effect relationships topredict future transactions or spending patterns. For example,non-transactional data may include the geographic location of a newsevent, the date of an event from an events calendar, the name of aperformer for an upcoming concert, etc. The non-transactional data canbe obtained from various sources, such as newspapers, websites, blogs,social networking sites, etc.

In one embodiment, when the cause and effect relationships between thetransactions and non-transactional events are known (e.g., based onprior research results, domain knowledge, expertise), the relationshipscan be used in predictive models to predict future transactions orspending patterns, based on events that occurred recently or arehappening in real-time.

In one embodiment, the non-transactional data relates to events thathappened in a geographical area local to the user (101) that performedthe respective transactions. In one embodiment, a geographical area islocal to the user (101) when the distance from the user (101) tolocations in the geographical area is within a convenient range fordaily or regular travel, such as 20, 50 or 100 miles from an address ofthe user (101), or within the same city or zip code area of an addressof the user (101). Examples of analyses of local non-transactional datain connection with transaction data (109) in one embodiment are providedin U.S. patent application Ser. No. 12/614,603, filed Nov. 9, 2009,published as U.S. Pat. App. Pub. No. 2011/0054981, and entitled“Analyzing Local Non-Transactional Data with Transactional Data inPredictive Models,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, the non-transactional data is not limited to localnon-transactional data. For example, national non-transactional data canalso be used.

In one embodiment, the transaction records (301) are analyzed infrequency domain to identify periodic features in spending events. Theperiodic features in the past transaction records (301) can be used topredict the probability of a time window in which a similar transactionwill occur. For example, the analysis of the transaction data (109) canbe used to predict when a next transaction having the periodic featurewill occur, with which merchant, the probability of a repeatedtransaction with a certain amount, the probability of exception, theopportunity to provide an advertisement or offer such as a coupon, etc.In one embodiment, the periodic features are detected through countingthe number of occurrences of pairs of transactions that occurred withina set of predetermined time intervals and separating the transactionpairs based on the time intervals. Some examples and techniques for theprediction of future transactions based on the detection of periodicfeatures in one embodiment are provided in U.S. patent application Ser.No. 12/773,770, filed May 4, 2010, published as 2010/0280882, andentitled “Frequency-Based Transaction Prediction and Processing,” thedisclosure of which is hereby incorporated herein by reference.

Techniques and details of predictive modeling in one embodiment areprovided in U.S. Pat. Nos. 6,119,103, 6,018,723, 6,658,393, 6,598,030,and 7,227,950, the disclosures of which are hereby incorporated hereinby reference.

In one embodiment, offers are based on the point-of-service to offereedistance to allow the user (101) to obtain in-person services. In oneembodiment, the offers are selected based on transaction history andshopping patterns in the transaction data (109) and/or the distancebetween the user (101) and the merchant. In one embodiment, offers areprovided in response to a request from the user (101), or in response toa detection of the location of the user (101). Examples and details ofat least one embodiment are provided in U.S. patent application Ser. No.11/767,218, filed Jun. 22, 2007, assigned Pub. No. 2008/0319843, andentitled “Supply of Requested Offer Based on Point-of Service to OffereeDistance,” U.S. patent application Ser. No. 11/755,575, filed May 30,2007, assigned Pub. No. 2008/0300973, and entitled “Supply of RequestedOffer Based on Offeree Transaction History,” U.S. patent applicationSer. No. 11/855,042, filed Sep. 13, 2007, assigned Pub. No.2009/0076896, and entitled “Merchant Supplied Offer to a Consumer withina Predetermined Distance,” U.S. patent application Ser. No. 11/855,069,filed Sep. 13, 2007, assigned Pub. No. 2009/0076925, and entitled“Offeree Requested Offer Based on Point-of Service to Offeree Distance,”and U.S. patent application Ser. No. 12/428,302, filed Apr. 22, 2009,assigned Pub. No. 2010/0274627, and entitled “Receiving an AnnouncementTriggered by Location Data,” the disclosures of which applications arehereby incorporated herein by reference.

Targeting Advertisement

In FIG. 1, an advertisement selector (133) prioritizes, generates,selects, adjusts, and/or customizes the available advertisement data(135) to provide user specific advertisement data (119) based at leastin part on the user specific profile (131). The advertisement selector(133) uses the user specific profile (131) as a filter and/or a set ofcriteria to generate, identify, select and/or prioritize advertisementdata for the user (101). A media controller (115) delivers the userspecific advertisement data (119) to the point of interaction (107) forpresentation to the user (101) as the targeted and/or personalizedadvertisement.

In one embodiment, the user data (125) includes the characterization ofthe context at the point of interaction (107). Thus, the use of the userspecific profile (131), selected using the user data (125), includes theconsideration of the context at the point of interaction (107) inselecting the user specific advertisement data (119).

In one embodiment, in selecting the user specific advertisement data(119), the advertisement selector (133) uses not only the user specificprofile (131), but also information regarding the context at the pointof interaction (107). For example, in one embodiment, the user data(125) includes information regarding the context at the point ofinteraction (107); and the advertisement selector (133) explicitly usesthe context information in the generation or selection of the userspecific advertisement data (119).

In one embodiment, the advertisement selector (133) may query forspecific information regarding the user (101) before providing the userspecific advertisement data (119). The queries may be communicated tothe operator of the transaction handler (103) and, in particular, to thetransaction handler (103) or the profile generator (121). For example,the queries from the advertisement selector (133) may be transmitted andreceived in accordance with an application programming interface orother query interface of the transaction handler (103), the profilegenerator (121) or the portal (143) of the transaction handler (103).

In one embodiment, the queries communicated from the advertisementselector (133) may request intelligence information regarding the user(101) at any level of specificity (e.g., segment level, individuallevel). For example, the queries may include a request for a certainfield or type of information in a cardholder's aggregated spendingprofile (341). As another example, the queries may include a request forthe spending level of the user (101) in a certain merchant category overa prior time period (e.g., six months).

In one embodiment, the advertisement selector (133) is operated by anentity that is separate from the entity that operates the transactionhandler (103). For example, the advertisement selector (133) may beoperated by a search engine, a publisher, an advertiser, an ad network,or an online merchant. The user specific profile (131) is provided tothe advertisement selector (133) to assist in the customization of theuser specific advertisement data (119).

In one embodiment, advertising is targeted based on shopping patterns ina merchant category (e.g., as represented by a Merchant Category Code(MCC)) that has high correlation of spending propensity with othermerchant categories (e.g., other MCCs). For example, in the context of afirst MCC for a targeted audience, a profile identifying second MCCsthat have high correlation of spending propensity with the first MCC canbe used to select advertisements for the targeted audience.

In one embodiment, the aggregated spending profile (341) is used toprovide intelligence information about the spending patterns,preferences, and/or trends of the user (101). For example, a predictivemodel can be established based on the aggregated spending profile (341)to estimate the needs of the user (101). For example, the factor values(344) and/or the cluster ID (343) in the aggregated spending profile(341) can be used to determine the spending preferences of the user(101). For example, the channel distribution (345) in the aggregatedspending profile (341) can be used to provide a customized offertargeted for a particular channel, based on the spending patterns of theuser (101).

In one embodiment, mobile advertisements, such as offers and coupons,are generated and disseminated based on aspects of prior purchases, suchas timing, location, and nature of the purchases, etc. In oneembodiment, the size of the benefit of the offer or coupon is based onpurchase volume or spending amount of the prior purchase and/or thesubsequent purchase that may qualify for the redemption of the offer.Further details and examples of one embodiment are provided in U.S.patent application Ser. No. 11/960,162, filed Dec. 19, 2007, assignedPub. No. 2008/0201226, and entitled “Mobile Coupon Method and PortableConsumer Device for Utilizing same,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, conditional rewards are provided to the user (101);and the transaction handler (103) monitors the transactions of the user(101) to identify redeemable rewards that have satisfied the respectiveconditions. In one embodiment, the conditional rewards are selectedbased on transaction data (109). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 11/862,487,filed Sep. 27, 2007 and entitled “Consumer Specific ConditionalRewards,” the disclosure of which is hereby incorporated herein byreference. The techniques to detect the satisfied conditions ofconditional rewards can also be used to detect the transactions thatsatisfy the conditions specified to locate the transactions that resultfrom online activities, such as online advertisements, searches, etc.,to correlate the transactions with the respective online activities.

Further details about targeted offer delivery in one embodiment areprovided in U.S. patent application Ser. No. 12/185,332, filed Aug. 4,2008, assigned Pub. No. 2010/0030644, and entitled “Targeted Advertisingby Payment Processor History of Cashless Acquired Merchant Transactionon Issued Consumer Account,” and in U.S. patent application Ser. No.12/849,793, filed Aug. 3, 2010, assigned Pub. No. 2011/0035280, andentitled “Systems and Methods for Targeted Advertisement Delivery,” thedisclosures of which applications are hereby incorporated herein byreference.

Profile Matching

In FIG. 1, the user tracker (113) obtains and generates contextinformation about the user (101) at the point of interaction (107),including user data (125) that characterizes and/or identifies the user(101). The profile selector (129) selects a user specific profile (131)from the set of transaction profiles (127) generated by the profilegenerator (121), based on matching the characteristics of thetransaction profiles (127) and the characteristics of the user data(125). For example, the user data (125) indicates a set ofcharacteristics of the user (101); and the profile selector (129)selects the user specific profile (131) for a particular user or groupof users that best matches the set of characteristics specified by theuser data (125).

In one embodiment, the profile selector (129) receives the transactionprofiles (127) in a batch mode. The profile selector (129) selects theuser specific profile (131) from the batch of transaction profiles (127)based on the user data (125). Alternatively, the profile generator (121)generates the transaction profiles (127) in real-time; and the profileselector (129) uses the user data (125) to query the profile generator(121) to generate the user specific profile (131) in real-time, or justin time. The profile generator (121) generates the user specific profile(131) that best matches the user data (125).

In one embodiment, the user tracker (113) identifies the user (101)based on the user's activity on the transaction terminal (105) (e.g.,having visited a set of websites, currently visiting a type of webpages, search behavior, etc.).

In one embodiment, the user data (125) includes an identifier of theuser (101), such as a global unique identifier (GUID), a personalaccount number (PAN) (e.g., credit card number, debit card number, orother card account number), or other identifiers that uniquely andpersistently identify the user (101) within a set of identifiers of thesame type. Alternatively, the user data (125) may include otheridentifiers, such as an Internet Protocol (IP) address of the user(101), a name or user name of the user (101), or a browser cookie ID,which identify the user (101) in a local, temporary, transient and/oranonymous manner. Some of these identifiers of the user (101) may beprovided by publishers, advertisers, ad networks, search engines,merchants, or the user tracker (113). In one embodiment, suchidentifiers are correlated to the user (101) based on the overlapping orproximity of the time period of their usage to establish anidentification reference table.

In one embodiment, the identification reference table is used toidentify the account information (142) (e.g., account number (302))based on characteristics of the user (101) captured in the user data(125), such as browser cookie ID, IP addresses, and/or timestamps on theusage of the IP addresses. In one embodiment, the identificationreference table is maintained by the operator of the transaction handler(103). Alternatively, the identification reference table is maintainedby an entity other than the operator of the transaction handler (103).

In one embodiment, the user tracker (113) determines certaincharacteristics of the user (101) to describe a type or group of usersof which the user (101) is a member. The transaction profile of thegroup is used as the user specific profile (131). Examples of suchcharacteristics include geographical location or neighborhood, types ofonline activities, specific online activities, or merchant propensity.In one embodiment, the groups are defined based on aggregate information(e.g., by time of day, or household), or segment (e.g., by cluster,propensity, demographics, cluster IDs, and/or factor values). In oneembodiment, the groups are defined in part via one or more socialnetworks. For example, a group may be defined based on social distancesto one or more users on a social network website, interactions betweenusers on a social network website, and/or common data in social networkprofiles of the users in the social network website.

In one embodiment, the user data (125) may match different profiles at adifferent granularity or resolution (e.g., account, user, family,company, neighborhood, etc.), with different degrees of certainty. Theprofile selector (129) and/or the profile generator (121) may determineor select the user specific profile (131) with the finest granularity orresolution with acceptable certainty. Thus, the user specific profile(131) is most specific or closely related to the user (101).

In one embodiment, the advertisement selector (133) uses further data inprioritizing, selecting, generating, customizing and adjusting the userspecific advertisement data (119). For example, the advertisementselector (133) may use search data in combination with the user specificprofile (131) to provide benefits or offers to a user (101) at the pointof interaction (107). For example, the user specific profile (131) canbe used to personalize the advertisement, such as adjusting theplacement of the advertisement relative to other advertisements,adjusting the appearance of the advertisement, etc.

Browser Cookie

In one embodiment, the user data (125) uses browser cookie informationto identify the user (101). The browser cookie information is matched toaccount information (142) or the account number (302) to identify theuser specific profile (131), such as aggregated spending profile (341),to present effective, timely, and relevant marketing information to theuser (101) via the preferred communication channel (e.g., mobilecommunications, web, mail, email, point-of-sale (POS) terminal, etc.)within a window of time that could influence the spending behavior ofthe user (101). Based on the transaction data (109), the user specificprofile (131) can improve audience targeting for online advertising.Thus, customers will get better advertisements and offers presented tothem; and the advertisers will achieve better return-on-investment fortheir advertisement campaigns.

In one embodiment, the browser cookie that identifies the user (101) inonline activities, such as web browsing, online searching, and usingsocial networking applications, can be matched to an identifier of theuser (101) in account data (111), such as the account number (302) of afinancial payment card of the user (101) or the account information(142) of the account identification device (141) of the user (101). Inone embodiment, the identifier of the user (101) can be uniquelyidentified via matching IP address, timestamp, cookie ID and/or otheruser data (125) observed by the user tracker (113).

In one embodiment, a look up table is used to map browser cookieinformation (e.g., IP address, timestamp, cookie ID) to the account data(111) that identifies the user (101) in the transaction handler (103).The look up table may be established via correlating overlapping orcommon portions of the user data (125) observed by different entities ordifferent user trackers (113).

For example, in one embodiment, a first user tracker (113) observes thecard number of the user (101) at a particular IP address for a timeperiod identified by a timestamp (e.g., via an online payment process);and a second user tracker (113) observes the user (101) having a cookieID at the same IP address for a time period near or overlapping with thetime period observed by the first user tracker (113). Thus, the cookieID as observed by the second user tracker (113) can be linked to thecard number of the user (101) as observed by the first user tracker(113). The first user tracker (113) may be operated by the same entityoperating the transaction handler (103) or by a different entity. Oncethe correlation between the cookie ID and the card number is establishedvia a database or a look up table, the cookie ID can be subsequentlyused to identify the card number of the user (101) and the account data(111).

In one embodiment, the portal (143) is configured to observe a cardnumber of a user (101) while the user (101) uses an IP address to makean online transaction. Thus, the portal (143) can identify a consumeraccount (146) based on correlating an IP address used to identify theuser (101) and IP addresses recorded in association with the consumeraccount (146).

For example, in one embodiment, when the user (101) makes a paymentonline by submitting the account information (142) to the transactionterminal (105) (e.g., an online store), the transaction handler (103)obtains the IP address from the transaction terminal (105) via theacquirer processor (147). The transaction handler (103) stores data toindicate the use of the account information (142) at the IP address atthe time of the transaction request. When an IP address in the queryreceived in the portal (143) matches the IP address previously recordedby the transaction handler (103), the portal (143) determines that theuser (101) identified by the IP address in the request is the same user(101) associated with the account used in the transaction initiated atthe IP address. In one embodiment, a match is found when the time of thequery request is within a predetermined time period from the transactionrequest, such as a few minutes, one hour, a day, etc. In one embodiment,the query may also include a cookie ID representing the user (101).Thus, through matching the IP address, the cookie ID is associated withthe account information (142) in a persistent way.

In one embodiment, the portal (143) obtains the IP address of the onlinetransaction directly. For example, in one embodiment, a user (101)chooses to use a password in the account data (111) to protect theaccount information (142) for online transactions. When the accountinformation (142) is entered into the transaction terminal (105) (e.g.,an online store or an online shopping cart system), the user (101) isconnected to the portal (143) for the verification of the password(e.g., via a pop up window, or via redirecting the web browser of theuser (101)). The transaction handler (103) accepts the transactionrequest after the password is verified via the portal (143). Throughthis verification process, the portal (143) and/or the transactionhandler (103) obtain the IP address of the user (101) at the time theaccount information (142) is used.

In one embodiment, the web browser of the user (101) communicates theuser-provided password to the portal (143) directly without goingthrough the transaction terminal (105) (e.g., the server of themerchant). Alternatively, the transaction terminal (105) and/or theacquirer processor (147) may relay the password communication to theportal (143) or the transaction handler (103).

In one embodiment, the portal (143) is configured to identify theconsumer account (146) based on the IP address identified in the userdata (125) through mapping the IP address to a street address. Forexample, in one embodiment, the user data (125) includes an IP addressto identify the user (101); and the portal (143) can use a service tomap the IP address to a street address. For example, an Internet serviceprovider knows the street address of the currently assigned IP address.Once the street address is identified, the portal (143) can use theaccount data (111) to identify the consumer account (146) that has acurrent address at the identified street address. Once the consumeraccount (146) is identified, the portal (143) can provide a transactionprofile (131) specific to the consumer account (146) of the user (101).

In one embodiment, the portal (143) uses a plurality of methods toidentify consumer accounts (146) based on the user data (125). Theportal (143) combines the results from the different methods todetermine the most likely consumer account (146) for the user data(125).

Details about the identification of consumer account (146) based on userdata (125) in one embodiment are provided in U.S. patent applicationSer. No. 12/849,798, filed Aug. 3, 2010, published as U.S. Pat. App.Pub. No. 2011/0093327, and entitled “Systems and Methods to MatchIdentifiers,” the disclosure of which is hereby incorporated herein byreference.

Close the Loop

In one embodiment, the correlator (117) is used to “close the loop” forthe tracking of consumer behavior across an on-line activity and an“off-line” activity that results at least in part from the on-lineactivity. In one embodiment, online activities, such as searching, webbrowsing, social networking, and/or consuming online advertisements, arecorrelated with respective transactions to generate the correlationresult (123) in FIG. 1. The respective transactions may occur offline,in “brick and mortar” retail stores, or online but in a context outsidethe online activities, such as a credit card purchase that is performedin a way not visible to a search company that facilitates the searchactivities.

In one embodiment, the correlator (117) is to identify transactionsresulting from searches or online advertisements. For example, inresponse to a query about the user (101) from the user tracker (113),the correlator (117) identifies an offline transaction performed by theuser (101) and sends the correlation result (123) about the offlinetransaction to the user tracker (113), which allows the user tracker(113) to combine the information about the offline transaction and theonline activities to provide significant marketing advantages.

For example, a marketing department could correlate an advertisingbudget to actual sales. For example, a marketer can use the correlationresult (123) to study the effect of certain prioritization strategies,customization schemes, etc. on the impact on the actual sales. Forexample, the correlation result (123) can be used to adjust orprioritize advertisement placement on a website, a search engine, asocial networking site, an online marketplace, or the like.

In one embodiment, the profile generator (121) uses the correlationresult (123) to augment the transaction profiles (127) with dataindicating the rate of conversion from searches or advertisements topurchase transactions. In one embodiment, the correlation result (123)is used to generate predictive models to determine what a user (101) islikely to purchase when the user (101) is searching using certainkeywords or when the user (101) is presented with an advertisement oroffer. In one embodiment, the portal (143) is configured to report thecorrelation result (123) to a partner, such as a search engine, apublisher, or a merchant, to allow the partner to use the correlationresult (123) to measure the effectiveness of advertisements and/orsearch result customization, to arrange rewards, etc.

Illustratively, a search engine entity may display a search page withparticular advertisements for flat panel televisions produced bycompanies A, B, and C. The search engine entity may then compare theparticular advertisements presented to a particular consumer withtransaction data of that consumer and may determine that the consumerpurchased a flat panel television produced by Company B. The searchengine entity may then use this information and other informationderived from the behavior of other consumers to determine theeffectiveness of the advertisements provided by companies A, B, and C.The search engine entity can determine if the placement, appearance, orother characteristic of the advertisement results in actual increasedsales. Adjustments to advertisements (e.g., placement, appearance, etc.)may be made to facilitate maximum sales.

In one embodiment, the correlator (117) matches the online activitiesand the transactions based on matching the user data (125) provided bythe user tracker (113) and the records of the transactions, such astransaction data (109) or transaction records (301). In anotherembodiment, the correlator (117) matches the online activities and thetransactions based on the redemption of offers/benefits provided in theuser specific advertisement data (119).

In one embodiment, the portal (143) is configured to receive a set ofconditions and an identification of the user (101), determine whetherthere is any transaction of the user (101) that satisfies the set ofconditions, and if so, provide indications of the transactions thatsatisfy the conditions and/or certain details about the transactions,which allows the requester to correlate the transactions with certainuser activities, such as searching, web browsing, consumingadvertisements, etc.

In one embodiment, the requester may not know the account number (302)of the user (101); and the portal (143) is to map the identifierprovided in the request to the account number (302) of the user (101) toprovide the requested information. Examples of the identifier beingprovided in the request to identify the user (101) include anidentification of an iFrame of a web page visited by the user (101), abrowser cookie ID, an IP address and the day and time corresponding tothe use of the IP address, etc.

The information provided by the portal (143) can be used in pre-purchasemarketing activities, such as customizing content or offers,prioritizing content or offers, selecting content or offers, etc., basedon the spending pattern of the user (101). The content that iscustomized, prioritized, selected, or recommended may be the searchresults, blog entries, items for sale, etc.

The information provided by the portal (143) can be used inpost-purchase activities. For example, the information can be used tocorrelate an offline purchase with online activities. For example, theinformation can be used to determine purchases made in response to mediaevents, such as television programs, advertisements, news announcements,etc.

Details about profile delivery, online activity to offline purchasetracking, techniques to identify the user specific profile (131) basedon user data (125) (such as IP addresses), and targeted delivery ofadvertisement/offer/benefit in some embodiments are provided in U.S.patent application Ser. No. 12/849,789, filed Aug. 3, 2010, published asU.S. Pat. App. Pub. No. 2011/0035278, and entitled “Systems and Methodsfor Closing the Loop between Online Activities and Offline Purchases,”the disclosure of which application is incorporated herein by reference.

Matching Advertisement & Transaction

In one embodiment, the correlator (117) is configured to receiveinformation about the user specific advertisement data (119), monitorthe transaction data (109), identify transactions that can be consideredresults of the advertisement corresponding to the user specificadvertisement data (119), and generate the correlation result (123), asillustrated in FIG. 1.

When the advertisement and the corresponding transaction both occur inan online checkout process, a website used for the online checkoutprocess can be used to correlate the transaction and the advertisement.However, the advertisement and the transaction may occur in separateprocesses and/or under control of different entities (e.g., when thepurchase is made offline at a retail store, whereas the advertisement ispresented outside the retail store). In one embodiment, the correlator(117) uses a set of correlation criteria to identify the transactionsthat can be considered as the results of the advertisements.

In one embodiment, the correlator (117) identifies the transactionslinked or correlated to the user specific advertisement data (119) basedon various criteria. For example, the user specific advertisement data(119) may include a coupon offering a benefit contingent upon a purchasemade according to the user specific advertisement data (119). The use ofthe coupon identifies the user specific advertisement data (119), andthus allows the correlator (117) to correlate the transaction with theuser specific advertisement data (119).

In one embodiment, the user specific advertisement data (119) isassociated with the identity or characteristics of the user (101), suchas global unique identifier (GUID), personal account number (PAN),alias, IP address, name or user name, geographical location orneighborhood, household, user group, and/or user data (125). Thecorrelator (117) can link or match the transactions with theadvertisements based on the identity or characteristics of the user(101) associated with the user specific advertisement data (119). Forexample, the portal (143) may receive a query identifying the user data(125) that tracks the user (101) and/or characteristics of the userspecific advertisement data (119); and the correlator (117) identifiesone or more transactions matching the user data (125) and/or thecharacteristics of the user specific advertisement data (119) togenerate the correlation result (123).

In one embodiment, the correlator (117) identifies the characteristicsof the transactions and uses the characteristics to search foradvertisements that match the transactions. Such characteristics mayinclude GUID, PAN, IP address, card number, browser cookie information,coupon, alias, etc.

In FIG. 1, the profile generator (121) uses the correlation result (123)to enhance the transaction profiles (127) generated from the profilegenerator (121). The correlation result (123) provides details onpurchases and/or indicates the effectiveness of the user specificadvertisement data (119).

In one embodiment, the correlation result (123) is used to demonstrateto the advertisers the effectiveness of the advertisements, to processincentive or rewards associated with the advertisements, to obtain atleast a portion of advertisement revenue based on the effectiveness ofthe advertisements, to improve the selection of advertisements, etc.

Coupon Matching

In one embodiment, the correlator (117) identifies a transaction that isa result of an advertisement (e.g., 119) when an offer or benefitprovided in the advertisement is redeemed via the transaction handler(103) in connection with a purchase identified in the advertisement.

For example, in one embodiment, when the offer is extended to the user(101), information about the offer can be stored in association with theaccount of the user (101) (e.g., as part of the account data (111)). Theuser (101) may visit the portal (143) of the transaction handler (103)to view the stored offer.

The offer stored in the account of the user (101) may be redeemed viathe transaction handler (103) in various ways. For example, in oneembodiment, the correlator (117) may download the offer to thetransaction terminal (105) via the transaction handler (103) when thecharacteristics of the transaction at the transaction terminal (105)match the characteristics of the offer.

After the offer is downloaded to the transaction terminal (105), thetransaction terminal (105) automatically applies the offer when thecondition of the offer is satisfied in one embodiment. Alternatively,the transaction terminal (105) allows the user (101) to selectivelyapply the offers downloaded by the correlator (117) or the transactionhandler (103). In one embodiment, the correlator (117) sends remindersto the user (101) at a separate point of interaction (107) (e.g., amobile phone) to remind the user (101) to redeem the offer. In oneembodiment, the transaction handler (103) applies the offer (e.g., viastatement credit), without having to download the offer (e.g., coupon)to the transaction terminal (105). Examples and details of redeemingoffers via statement credit are provided in U.S. patent application Ser.No. 12/566,350, filed Sep. 24, 2009, published as U.S. Pat. App. Pub.No. 2010/0114686, and entitled “Real-Time Statement Credits andNotifications,” the disclosure of which is hereby incorporated herein byreference.

In one embodiment, the offer is captured as an image and stored inassociation with the account of the user (101). Alternatively, the offeris captured in a text format (e.g., a code and a set of criteria),without replicating the original image of the coupon.

In one embodiment, when the coupon is redeemed, the advertisementpresenting the coupon is correlated with a transaction in which thecoupon is redeemed, and/or is determined to have resulted in atransaction. In one embodiment, the correlator (117) identifiesadvertisements that have resulted in purchases, without having toidentify the specific transactions that correspond to theadvertisements.

Details about offer redemption via the transaction handler (103) in oneembodiment are provided in U.S. patent application Ser. No. 12/849,801,filed Aug. 3, 2010, published as U.S. Pat. App. Pub. No. 2011/0125565,and entitled “Systems and Methods for Multi-Channel Offer Redemption,”the disclosure of which is hereby incorporated herein by reference.

On ATM & POS Terminal

In one example, the transaction terminal (105) is an automatic tellermachine (ATM), which is also the point of interaction (107). When theuser (101) approaches the ATM to make a transaction (e.g., to withdrawcash via a credit card or debit card), the ATM transmits accountinformation (142) to the transaction handler (103). The accountinformation (142) can also be considered as the user data (125) toselect the user specific profile (131). The user specific profile (131)can be sent to an advertisement network to query for a targetedadvertisement. After the advertisement network matches the user specificprofile (131) with user specific advertisement data (119) (e.g., atargeted advertisement), the transaction handler (103) may send theadvertisement to the ATM, together with the authorization for cashwithdrawal.

In one embodiment, the advertisement shown on the ATM includes a couponthat offers a benefit that is contingent upon the user (101) making apurchase according to the advertisement. The user (101) may view theoffer presented on a white space on the ATM screen and select to load orstore the coupon in a storage device of the transaction handler (103)under the account of the user (101). The transaction handler (103)communicates with the bank to process the cash withdrawal. After thecash withdrawal, the ATM prints the receipt, which includes aconfirmation of the coupon, or a copy of the coupon. The user (101) maythen use the coupon printed on the receipt. Alternatively, when the user(101) uses the same account to make a relevant purchase, the transactionhandler (103) may automatically apply the coupon stored under theaccount of the user (101), automatically download the coupon to therelevant transaction terminal (105), or transmit the coupon to themobile phone of the user (101) to allow the user (101) to use the couponvia a display of the coupon on the mobile phone. The user (101) mayvisit a web portal (143) of the transaction handler (103) to view thestatus of the coupons collected in the account of the user (101).

In one embodiment, the advertisement is forwarded to the ATM via thedata stream for authorization. In another embodiment, the ATM makes aseparate request to a server of the transaction handler (103) (e.g., aweb portal) to obtain the advertisement. Alternatively, or incombination, the advertisement (including the coupon) is provided to theuser (101) at separate, different points of interactions, such as via atext message to a mobile phone of the user (101), via an email, via abank statement, etc.

Details of presenting targeted advertisements on ATMs based onpurchasing preferences and location data in one embodiment are providedin U.S. patent application Ser. No. 12/266,352, filed Nov. 6, 2008,published as U.S. Pat. App. Pub. No. 2010/0114677, and entitled “SystemIncluding Automated Teller Machine with Data Bearing Medium,” thedisclosure of which is hereby incorporated herein by reference.

In another example, the transaction terminal (105) is a POS terminal atthe checkout station in a retail store (e.g., a self-service checkoutregister). When the user (101) pays for a purchase via a payment card(e.g., a credit card or a debit card), the transaction handler (103)provides a targeted advertisement having a coupon obtained from anadvertisement network. The user (101) may load the coupon into theaccount of the payment card and/or obtain a hardcopy of the coupon fromthe receipt. When the coupon is used in a transaction, the advertisementis linked to the transaction.

Details of presenting targeted advertisements during the process ofauthorizing a financial payment card transaction in one embodiment areprovided in U.S. patent application Ser. No. 11/799,549, filed May 1,2007, assigned Pub. No. 2008/0275771, and entitled “Merchant TransactionBased Advertising,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, the user specific advertisement data (119), such asoffers or coupons, is provided to the user (101) via the transactionterminal (105) in connection with an authorization message during theauthorization of a transaction processed by the transaction handler(103). The authorization message can be used to communicate the rewardsqualified for by the user (101) in response to the current transaction,the status and/or balance of rewards in a loyalty program, etc. Examplesand details related to the authorization process in one embodiment areprovided in U.S. patent application Ser. No. 11/266,766, filed Nov. 2,2005, assigned Pub. No. 2007/0100691, and entitled “Method and Systemfor Conducting Promotional Programs,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, when the user (101) is conducting a transaction witha first merchant via the transaction handler (103), the transactionhandler (103) may determine whether the characteristics of thetransaction satisfy the conditions specified for an announcement, suchas an advertisement, offer or coupon, from a second merchant. If theconditions are satisfied, the transaction handler (103) provides theannouncement to the user (101). In one embodiment, the transactionhandler (103) may auction the opportunity to provide the announcementsto a set of merchants. Examples and details related to the delivery ofsuch announcements in one embodiment are provided in U.S. patentapplication Ser. No. 12/428,241, filed Apr. 22, 2009, published as U.S.Pat. App. Pub. No. 2010/0274625, and entitled “Targeting MerchantAnnouncements Triggered by Consumer Activity Relative to a SurrogateMerchant,” the disclosure of which is hereby incorporated herein byreference.

Details about delivering advertisements at a point of interaction thatis associated with user transaction interactions in one embodiment areprovided in U.S. patent application Ser. No. 12/849,791, filed Aug. 3,2010, published as U.S. Pat. App. Pub. No. 2011/0087550, and entitled“Systems and Methods to Deliver Targeted Advertisements to Audience,”the disclosure of which is hereby incorporated herein by reference.

On Third Party Site

In a further example, the user (101) may visit a third party website,which is the point of interaction (107) in FIG. 1. The third partywebsite may be a web search engine, a news website, a blog, a socialnetwork site, etc. The behavior of the user (101) at the third partywebsite may be tracked via a browser cookie, which uses a storage spaceof the browser to store information about the user (101) at the thirdparty website. Alternatively, or in combination, the third party websiteuses the server logs to track the activities of the user (101). In oneembodiment, the third party website may allow an advertisement networkto present advertisements on portions of the web pages. Theadvertisement network tracks the user's behavior using its server logsand/or browser cookies. For example, the advertisement network may use abrowser cookie to identify a particular user across multiple websites.Based on the referral uniform resource locators (URL) that cause theadvertisement network to load advertisements in various web pages, theadvertisement network can determine the online behavior of the user(101) via analyzing the web pages that the user (101) has visited. Basedon the tracked online activities of the user (101), the user data (125)that characterizes the user (101) can be formed to query the profilerselector (129) for a user specific profile (131).

In one embodiment, the cookie identity of the user (101) as trackedusing the cookie can be correlated to an account of the user (101), thefamily of the user (101), the company of the user (101), or other groupsthat include the user (101) as a member. Thus, the cookie identity canbe used as the user data (125) to obtain the user specific profile(131). For example, when the user (101) makes an online purchase from aweb page that contains an advertisement that is tracked with the cookieidentity, the cookie identity can be correlated to the onlinetransaction and thus to the account of the user (101). For example, whenthe user (101) visits a web page after authentication of the user (101),and the web page includes an advertisement from the advertisementnetwork, the cookie identity can be correlated to the authenticatedidentity of the user (101). For example, when the user (101) signs in toa web portal (e.g., 143) of the transaction handler (103) to access theaccount of the user (101), the cookie identity used by the advertisementnetwork on the web portal (e.g., 143) can be correlated to the accountof the user (101).

Other online tracking techniques can also be used to correlate thecookie identity of the user (101) with an identifier of the user (101)known by the profile selector (129), such as a GUID, PAN, accountnumber, customer number, social security number, etc. Subsequently, thecookie identity can be used to select the user specific profile (131).

Multiple Communications

In one embodiment, the entity operating the transaction handler (103)may provide intelligence for providing multiple communications regardingan advertisement. The multiple communications may be directed to two ormore points of interaction with the user (101).

For example, after the user (101) is provided with an advertisement viathe transaction terminal (105), reminders or revisions to theadvertisements can be sent to the user (101) via a separate point ofinteraction (107), such as a mobile phone, email, text message, etc. Forexample, the advertisement may include a coupon to offer the user (101)a benefit contingent upon a purchase. If the correlator (117) determinesthat the coupon has not been redeemed, the correlator (117) may send amessage to the mobile phone of the user (101) to remind the user (101)about the offer, and/or revise the offer.

Examples of multiple communications related to an offer in oneembodiment are provided in U.S. patent application Ser. No. 12/510,167,filed Jul. 27, 2009, published as U.S. Pat. App. Pub. No. 2011/0022424,and entitled “Successive Offer Communications with an Offer Recipient,”the disclosure of which is hereby incorporated herein by reference.

Auction Engine

In one embodiment, the transaction handler (103) provides a portal(e.g., 143) to allow various clients to place bids according to clusters(e.g., to target entities in the clusters for marketing, monitoring,researching, etc.)

For example, cardholders may register in a program to receive offers,such as promotions, discounts, sweepstakes, reward points, direct mailcoupons, email coupons, etc. The cardholders may register with issuers,or with the portal (143) of the transaction handler (103). Based on thetransaction data (109) or transaction records (301) and/or theregistration data, the profile generator (121) is to identify theclusters of cardholders and the values representing the affinity of thecardholders to the clusters. Various entities may place bids accordingto the clusters and/or the values to gain access to the cardholders,such as the user (101). For example, an issuer may bid on access tooffers; an acquirer and/or a merchant may bid on customer segments. Anauction engine receives the bids and awards segments and offers based onthe received bids. Thus, customers can get great deals; and merchantscan get customer traffic and thus sales.

Some techniques to identify a segment of users (101) for marketing areprovided in U.S. patent application Ser. No. 12/288,490, filed Oct. 20,2008, assigned Pub. No. 2009/0222323, and entitled “OpportunitySegmentation,” U.S. patent application Ser. No. 12/108,342, filed Apr.23, 2008, assigned Pub. No. 2009/0271305, and entitled “PaymentPortfolio Optimization,” and U.S. patent application Ser. No.12/108,354, filed Apr. 23, 2008, assigned Pub. No. 2009/0271327, andentitled “Payment Portfolio Optimization,” the disclosures of whichapplications are hereby incorporated herein by reference.

Social Network Validation

In one embodiment, the transaction data (109) is combined with socialnetwork data and/or search engine data to provide benefits (e.g.,coupons) to a consumer. For example, a data exchange apparatus mayidentify cluster data based upon consumer search engine data, socialnetwork data, and payment transaction data to identify like groups ofindividuals who would respond favorably to particular types of benefitssuch as coupons and statement credits. Advertisement campaigns may beformulated to target the cluster of consumers or cardholders.

In one embodiment, search engine data is combined with social networkdata and/or the transaction data (109) to evaluate the effectiveness ofthe advertisements and/or conversion pattern of the advertisements. Forexample, after a search engine displays advertisements about flat paneltelevisions to a consumer, a social network that is used by a consumermay provide information about a related purchase made by the consumer.For example, the blog of the consumer, and/or the transaction data(109), may indicate that the flat panel television purchased by theconsumer is from company B. Thus, the search engine data, the socialnetwork data and/or the transaction data (109) can be combined tocorrelate advertisements to purchases resulting from the advertisementsand to determine the conversion pattern of the advertisement presentedto the consumer. Adjustments to advertisements (e.g., placement,appearance, etc.) can be made to improve the effectiveness of theadvertisements and thus increase sales.

Loyalty Program

In one embodiment, the transaction handler (103) uses the account data(111) to store information for third party loyalty programs. Thetransaction handler (103) processes payment transactions made viafinancial transaction cards, such as credit cards, debit cards, bankingcards, etc.; and the financial transaction cards can be used as loyaltycards for the respective third party loyalty programs. Since the thirdparty loyalty programs are hosted on the transaction handler (103), theconsumers do not have to carry multiple, separate loyalty cards (e.g.,one for each merchant that offers a loyalty program); and the merchantsdo not have to incur a large setup and investment fee to establish theloyalty program. The loyalty programs hosted on the transaction handler(103) can provide flexible awards for consumers, retailers,manufacturers, issuers, and other types of business entities involved inthe loyalty programs. The integration of the loyalty programs into theaccounts of the customers on the transaction handler (103) allows newofferings, such as merchant cross-offerings or bundling of loyaltyofferings.

In one embodiment, an entity operating the transaction handler (103)hosts loyalty programs for third parties using the account data (111) ofthe users (e.g., 101). A third party, such as a merchant, retailer,manufacturer, issuer or other entity that is interested in promotingcertain activities and/or behaviors, may offer loyalty rewards onexisting accounts of consumers. The incentives delivered by the loyaltyprograms can drive behavior changes without the hassle of loyalty cardcreation. In one embodiment, the loyalty programs hosted via theaccounts of the users (e.g., 101) of the transaction handler (103) allowthe consumers to carry fewer cards and may provide more data to themerchants than traditional loyalty programs.

The loyalty programs integrated with the accounts of the users (e.g.,101) of the transaction handler (103) can provide tools to enable nimbleprograms that are better aligned for driving changes in consumerbehaviors across transaction channels (e.g., online, offline, via mobiledevices). The loyalty programs can be ongoing programs that accumulatebenefits for customers (e.g., points, miles, cash back), and/or programsthat provide one time benefits or limited time benefits (e.g., rewards,discounts, incentives).

FIG. 8 shows the structure of account data (111) for providing loyaltyprograms according to one embodiment. In FIG. 8, data related to a thirdparty loyalty program may include an identifier of the loyalty benefitofferor (183) that is linked to a set of loyalty program rules (185) andthe loyalty record (187) for the loyalty program activities of theaccount identifier (181). In one embodiment, at least part of the datarelated to the third party loyalty program is stored under the accountidentifier (181) of the user (101), such as the loyalty record (187).

FIG. 8 illustrates the data related to one third party loyalty programof a loyalty benefit offeror (183). In one embodiment, the accountidentifier (181) may be linked to multiple loyalty benefit offerors(e.g., 183), corresponding to different third party loyalty programs.

In one embodiment, a third party loyalty program of the loyalty benefitofferor (183) provides the user (101), identified by the accountidentifier (181), with benefits, such as discounts, rewards, incentives,cash back, gifts, coupons, and/or privileges.

In one embodiment, the association between the account identifier (181)and the loyalty benefit offeror (183) in the account data (111)indicates that the user (101) having the account identifier (181) is amember of the loyalty program. Thus, the user (101) may use the accountidentifier (181) to access privileges afforded to the members of theloyalty program, such as rights to access a member only area, facility,store, product or service, discounts extended only to members, oropportunities to participate in certain events, buy certain items, orreceive certain services reserved for members.

In one embodiment, it is not necessary to make a purchase to use theprivileges. The user (101) may enjoy the privileges based on the statusof being a member of the loyalty program. The user (101) may use theaccount identifier (181) to show the status of being a member of theloyalty program.

For example, the user (101) may provide the account identifier (181)(e.g., the account number of a credit card) to the transaction terminal(105) to initiate an authorization process for a special transactionwhich is designed to check the member status of the user (101), in amanner similar to using the account identifier (181) to initiate anauthorization process for a payment transaction. The special transactionis designed to verify the member status of the user (101) via checkingwhether the account data (111) is associated with the loyalty benefitofferor (183). If the account identifier (181) is associated with thecorresponding loyalty benefit offeror (183), the transaction handler(103) provides an approval indication in the authorization process toindicate that the user (101) is a member of the loyalty program. Theapproval indication can be used as a form of identification to allow theuser (101) to access member privileges, such as access to services,products, opportunities, facilities, discounts, permissions, etc., whichare reserved for members.

In one embodiment, when the account identifier (181) is used to identifythe user (101) as a member to access member privileges, the transactionhandler (103) stores information about the access of the correspondingmember privilege in loyalty record (187). The profile generator (121)may use the information accumulated in the loyalty record (187) toenhance transaction profiles (127) and provide the user (101) withpersonalized/targeted advertisements, with or without further offers ofbenefit (e.g., discounts, incentives, rebates, cash back, rewards,etc.).

In one embodiment, the association of the account identifier (181) andthe loyalty benefit offeror (183) also allows the loyalty benefitofferor (183) to access at least a portion of the account data (111)relevant to the loyalty program, such as the loyalty record (187) andcertain information about the user (101), such as name, address, andother demographic data.

In one embodiment, the loyalty program allows the user (101) toaccumulate benefits according to loyalty program rules (185), such asreward points, cash back, levels of discounts, etc. For example, theuser (101) may accumulate reward points for transactions that satisfythe loyalty program rules (185); and the user (101) may redeem thereward points for cash, gifts, discounts, etc. In one embodiment, theloyalty record (187) stores the accumulated benefits; and thetransaction handler (103) updates the loyalty record (187) associatedwith the loyalty benefit offeror (183) and the account identifier (181),when events that satisfy the loyalty program rules (185) occur.

In one embodiment, the accumulated benefits as indicated in the loyaltyrecord (187) can be redeemed when the account identifier (181) is usedto perform a payment transaction, when the payment transaction satisfiesthe loyalty program rules (185). For example, the user (101) may redeema number of points to offset or reduce an amount of the purchase price.

In one embodiment, when the user (101) uses the account identifier (181)to make purchases as a member, the merchant may further provideinformation about the purchases; and the transaction handler (103) canstore the information about the purchases as part of the loyalty record(187). The information about the purchases may identify specific itemsor services purchased by the member. For example, the merchant mayprovide the transaction handler (103) with purchase details atstock-keeping unit (SKU) level, which are then stored as part of theloyalty record (187). The loyalty benefit offeror (183) may use thepurchase details to study the purchase behavior of the user (101); andthe profile generator (121) may use the SKU level purchase details toenhance the transaction profiles (127).

In one embodiment, the SKU level purchase details are requested from themerchants or retailers via authorization responses, when the account(146) of the user (101) is enrolled in a loyalty program that allows thetransaction handler (103) (and/or the issuer processor (145)) to collectthe purchase details.

In one embodiment, the profile generator (121) may generate transactionprofiles (127) based on the loyalty record (187) and provide thetransaction profiles (127) to the loyalty benefit offeror (183) (orother entities when permitted).

In one embodiment, the loyalty benefit offeror (183) may use thetransaction profiles (e.g., 127 or 131) to select candidates formembership offering. For example, the loyalty program rules (185) mayinclude one or more criteria that can be used to identify whichcustomers are eligible for the loyalty program. The transaction handler(103) may be configured to automatically provide the qualified customerswith an offer of membership in the loyalty program when thecorresponding customers are performing transactions via the transactionhandler (103) and/or via points of interaction (107) accessible to theentity operating the transaction handler (103), such as ATMs, mobilephones, receipts, statements, websites, etc. The user (101) may acceptthe membership offer via responding to the advertisement. For example,the user (101) may load the membership into the account in the same wayas loading a coupon into the account of the user (101).

In one embodiment, the membership offer is provided as a coupon or isassociated with another offer of benefits, such as a discount, reward,etc. When the coupon or benefit is redeemed via the transaction handler(103), the account data (111) is updated to enroll the user (101) intothe corresponding loyalty program.

In one embodiment, a merchant may enroll a user (101) into a loyaltyprogram when the user (101) is making a purchase at the transactionterminal (105) of the merchant.

For example, when the user (101) is making a transaction at an ATM,performing a self-assisted check out on a POS terminal, or making apurchase transaction on a mobile phone or a computer, the user (101) maybe prompted to join a loyalty program, while the transaction is beingauthorized by the transaction handler (103). If the user (101) acceptsthe membership offer, the account data (111) is updated to have theaccount identifier (181) associated with the loyalty benefit offeror(183).

In one embodiment, the user (101) may be automatically enrolled in theloyalty program, when the profile of the user (101) satisfies a set ofconditions specified in the loyalty program rules (185). The user (101)may opt out of the loyalty program.

In one embodiment, the loyalty benefit offeror (183) may personalizeand/or target loyalty benefits based on the transaction profile (131)specific to or linked to the user (101). For example, the loyaltyprogram rules (185) may use the user specific profile (131) to selectgifts, rewards, or incentives for the user (101) (e.g., to redeembenefits, such as reward points, accumulated in the loyalty record(187)). The user specific profile (131) may be enhanced using theloyalty record (187), or generated based on the loyalty record (187).For example, the profile generator (121) may use a subset of transactiondata (109) associated with the loyalty record (187) to generate the userspecific profile (131), or provide more weight to the subset of thetransaction data (109) associated with the loyalty record (187) whilealso using other portions of the transaction data (109) in deriving theuser specific profile (131).

In one embodiment, the loyalty program may involve different entities.For example, a first merchant may offer rewards as discounts, or giftsfrom a second merchant that has a business relationship with the firstmerchant. For example, an entity may allow a user (101) to accumulateloyalty benefits (e.g., reward points) via purchase transactions at agroup of different merchants. For example, a group of merchants mayjointly offer a loyalty program, in which loyalty benefits (e.g., rewardpoints) can be accumulated from purchases at any of the merchants in thegroup and redeemable in purchases at any of the merchants.

In one embodiment, the information identifying the user (101) as amember of a loyalty program is stored on a server connected to thetransaction handler (103). Alternatively or in combination, theinformation identifying the user (101) as a member of a loyalty programcan also be stored in a financial transaction card (e.g., in the chip,or in the magnetic strip).

In one embodiment, loyalty program offerors (e.g., merchants,manufactures, issuers, retailers, clubs, organizations, etc.) cancompete with each other in making loyalty program related offers. Forexample, loyalty program offerors may place bids on loyalty programrelated offers; and the advertisement selector (133) (e.g., under thecontrol of the entity operating the transaction handler (103), or adifferent entity) may prioritize the offers based on the bids. When theoffers are accepted or redeemed by the user (101), the loyalty programofferors pay fees according to the corresponding bids. In oneembodiment, the loyalty program offerors may place an auto bid ormaximum bid, which specifies the upper limit of a bid; and the actualbid is determined to be the lowest possible bid that is larger than thebids of the competitors, without exceeding the upper limit.

In one embodiment, the offers are provided to the user (101) in responseto the user (101) being identified by the user data (125). If the userspecific profile (131) satisfies the conditions specified in the loyaltyprogram rules (185), the offer from the loyalty benefit offeror (183)can be presented to the user (101). When there are multiple offers fromdifferent offerors, the offers can be prioritized according to the bids.

In one embodiment, the offerors can place bids based on thecharacteristics that can be used as the user data (125) to select theuser specific profile (131). In another embodiment, the bids can beplaced on a set of transaction profiles (127).

In one embodiment, the loyalty program based offers are provided to theuser (101) just in time when the user (101) can accept and redeem theoffers. For example, when the user (101) is making a payment for apurchase from a merchant, an offer to enroll in a loyalty programoffered by the merchant or related offerors can be presented to the user(101). If the user (101) accepts the offer, the user (101) is entitledto receive member discounts for the purchase.

For example, when the user (101) is making a payment for a purchase froma merchant, a reward offer can be provided to the user (101) based onloyalty program rules (185) and the loyalty record (187) associated withthe account identifier (181) of the user (101) (e.g., the reward pointsaccumulated in a loyalty program). Thus, the user effort for redeemingthe reward points can be reduced; and the user experience can beimproved.

In one embodiment, a method to provide loyalty programs includes the useof a computing apparatus of a transaction handler (103). The computingapparatus processes a plurality of payment card transactions. After thecomputing apparatus receives a request to track transactions for aloyalty program, such as the loyalty program rules (185), the computingapparatus stores and updates loyalty program information in response totransactions occurring in the loyalty program. The computing apparatusprovides to a customer (e.g., 101) an offer of a benefit when thecustomer satisfies a condition defined in the loyalty program, such asthe loyalty program rules (185).

Examples of loyalty programs offered through collaboration betweencollaborative constituents in a payment processing system, including thetransaction handler (103) in one embodiment are provided in U.S. patentapplication Ser. No. 11/767,202, filed Jun. 22, 2007, assigned Pub. No.2008/0059302, and entitled “Loyalty Program Service,” U.S. patentapplication Ser. No. 11/848,112, filed Aug. 30, 2007, assigned Pub. No.2008/0059306, and entitled “Loyalty Program Incentive Determination,”and U.S. patent application Ser. No. 11/848,179, filed Aug. 30, 2007,assigned Pub. No. 2008/0059307, and entitled “Loyalty Program ParameterCollaboration,” the disclosures of which applications are herebyincorporated herein by reference.

Examples of processing the redemption of accumulated loyalty benefitsvia the transaction handler (103) in one embodiment are provided in U.S.patent application Ser. No. 11/835,100, filed Aug. 7, 2007, assignedPub. No. 2008/0059303, and entitled “Transaction Evaluation forProviding Rewards,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, the incentive, reward, or benefit provided in theloyalty program is based on the presence of correlated relatedtransactions. For example, in one embodiment, an incentive is providedif a financial payment card is used in a reservation system to make areservation and the financial payment card is subsequently used to payfor the reserved good or service. Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 11/945,907,filed Nov. 27, 2007, assigned Pub. No. 2008/0071587, and entitled“Incentive Wireless Communication Reservation,” the disclosure of whichis hereby incorporated herein by reference.

In one embodiment, the transaction handler (103) provides centralizedloyalty program management, reporting and membership services. In oneembodiment, membership data is downloaded from the transaction handler(103) to acceptance point devices, such as the transaction terminal(105). In one embodiment, loyalty transactions are reported from theacceptance point devices to the transaction handler (103); and the dataindicating the loyalty points, rewards, benefits, etc. are stored on theaccount identification device (141). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 10/401,504,filed Mar. 27, 2003, assigned Pub. No. 2004/0054581, and entitled“Network Centric Loyalty System,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, the portal (143) of the transaction handler (103) isused to manage reward or loyalty programs for entities such as issuers,merchants, etc. The cardholders, such as the user (101), are rewardedwith offers/benefits from merchants. The portal (143) and/or thetransaction handler (103) track the transaction records for themerchants for the reward or loyalty programs. Further details andexamples of one embodiment are provided in U.S. patent application Ser.No. 11/688,423, filed Mar. 20, 2007, assigned Pub. No. 2008/0195473, andentitled “Reward Program Manager,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, a loyalty program includes multiple entitiesproviding access to detailed transaction data, which allows theflexibility for the customization of the loyalty program. For example,issuers or merchants may sponsor the loyalty program to provide rewards;and the portal (143) and/or the transaction handler (103) stores theloyalty currency in the data warehouse (149). Further details andexamples of one embodiment are provided in U.S. patent application Ser.No. 12/177,530, filed Jul. 22, 2008, assigned Pub. No. 2009/0030793, andentitled “Multi-Vender Multi-Loyalty Currency Program,” the disclosureof which is hereby incorporated herein by reference.

In one embodiment, an incentive program is created on the portal (143)of the transaction handler (103). The portal (143) collects offers froma plurality of merchants and stores the offers in the data warehouse(149). The offers may have associated criteria for their distributions.The portal (143) and/or the transaction handler (103) may recommendoffers based on the transaction data (109). In one embodiment, thetransaction handler (103) automatically applies the benefits of theoffers during the processing of the transactions when the transactionssatisfy the conditions associated with the offers. In one embodiment,the transaction handler (103) communicates with transaction terminals(e.g., 105) to set up, customize, and/or update offers based on marketfocus, product categories, service categories, targeted consumerdemographics, etc. Further details and examples of one embodiment areprovided in U.S. patent application Ser. No. 12/413,097, filed Mar. 27,2009, assigned Pub. No. 2010-0049620, and entitled “Merchant DeviceSupport of an Integrated Offer Network,” the disclosure of which ishereby incorporated herein by reference.

In one embodiment, the transaction handler (103) is configured toprovide offers from merchants to the user (101) via the payment system,making accessing and redeeming the offers convenient for the user (101).The offers may be triggered by and/or tailored to a previoustransaction, and may be valid only for a limited period of time startingfrom the date of the previous transaction. If the transaction handler(103) determines that a subsequent transaction processed by thetransaction handler (103) meets the conditions for the redemption of anoffer, the transaction handler (103) may credit the consumer account(146) for the redemption of the offer and/or provide a notificationmessage to the user (101). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 12/566,350,filed Sep. 24, 2009, assigned Pub. No. 2010/0114686, and entitled“Real-Time Statement Credits and Notifications,” the disclosure of whichis hereby incorporated herein by reference.

Details on loyalty programs in one embodiment are provided in U.S.patent application Ser. No. 12/896,632, filed Oct. 1, 2010, assignedPub. No. 2011/0087530, and entitled “Systems and Methods to ProvideLoyalty Programs,” the disclosure of which is hereby incorporated hereinby reference.

SKU

In one embodiment, merchants generate stock-keeping unit (SKU) or otherspecific information that identifies the particular goods and servicespurchased by the user (101) or customer. The SKU information may beprovided to the operator of the transaction handler (103) that processedthe purchases. The operator of the transaction handler (103) may storethe SKU information as part of transaction data (109), and reflect theSKU information for a particular transaction in a transaction profile(127 or 131) associated with the person involved in the transaction.

When a user (101) shops at a traditional retail store or browses awebsite of an online merchant, an SKU-level profile associatedspecifically with the user (101) may be provided to select anadvertisement appropriately targeted to the user (101) (e.g., via mobilephones, POS terminals, web browsers, etc.). The SKU-level profile forthe user (101) may include an identification of the goods and serviceshistorically purchased by the user (101). In addition, the SKU-levelprofile for the user (101) may identify goods and services that the user(101) may purchase in the future. The identification may be based onhistorical purchases reflected in SKU-level profiles of otherindividuals or groups that are determined to be similar to the user(101). Accordingly, the return on investment for advertisers andmerchants can be greatly improved.

In one embodiment, the user specific profile (131) is an aggregatedspending profile (341) that is generated using the SKU-levelinformation. For example, in one embodiment, the factor values (344)correspond to factor definitions (331) that are generated based onaggregating spending in different categories of products and/orservices. A typical merchant offers products and/or services in manydifferent categories.

In one embodiment, the user (101) may enter into transactions withvarious online and “brick and mortar” merchants. The transactions mayinvolve the purchase of various goods and services. The goods andservices may be identified by SKU numbers or other information thatspecifically identifies the goods and services purchased by the user(101).

In one embodiment, the merchant may provide the SKU informationregarding the goods and services purchased by the user (101) (e.g.,purchase details at SKU level) to the operator of the transactionhandler (103). In one embodiment, the SKU information may be provided tothe operator of the transaction handler (103) in connection with aloyalty program, as described in more detail below. The SKU informationmay be stored as part of the transaction data (109) and associated withthe user (101). In one embodiment, the SKU information for itemspurchased in transactions facilitated by the operator of the transactionhandler (103) may be stored as transaction data (109) and associatedwith its associated purchaser. In one embodiment, the SKU level purchasedetails are requested from the merchants or retailers via authorizationresponses, when the account (146) of the user (101) is enrolled in aprogram that allows the transaction handler (103) (and/or the issuerprocessor (145)) to collect the purchase details.

In one embodiment, based on the SKU information and perhaps othertransaction data, the profile generator (121) may create an SKU-leveltransaction profile for the user (101). In one embodiment, based on theSKU information associated with the transactions for each personentering into transactions with the operator of the transaction handler(103), the profile generator (121) may create an SKU-level transactionprofile for each person.

In one embodiment, the SKU information associated with a group ofpurchasers may be aggregated to create an SKU-level transaction profilethat is descriptive of the group. The group may be defined based on oneor a variety of considerations. For example, the group may be defined bycommon demographic features of its members. As another example, thegroup may be defined by common purchasing patters of its members.

In one embodiment, the user (101) may later consider the purchase ofadditional goods and services. The user (101) may shop at a traditionalretailer or an online retailer. With respect to an online retailer, forexample, the user (101) may browse the website of an online retailer,publisher, or merchant. The user (101) may be associated with a browsercookie to, for example, identify the user (101) and track the browsingbehavior of the user (101).

In one embodiment, the retailer may provide the browser cookieassociated with the user (101) to the operator of the transactionhandler (103). Based on the browser cookie, the operator of thetransaction handler (103) may associate the browser cookie with apersonal account number of the user (101). The association may beperformed by the operator of the transaction handler (103) or anotherentity in a variety of manners such as, for example, using a look uptable.

Based on the personal account number, the profile selector (129) mayselect a user specific profile (131) that constitutes the SKU-levelprofile associated specifically with the user (101). The SKU-levelprofile may reflect the individual, prior purchases of the user (101)specifically, and/or the types of goods and services that the user (101)has purchased.

The SKU-level profile for the user (101) may also includeidentifications of goods and services the user (101) may purchase in thefuture. In one embodiment, the identifications may be used for theselection of advertisements for goods and services that may be ofinterest to the user (101). In one embodiment, the identifications forthe user (101) may be based on the SKU-level information associated withhistorical purchases of the user (101). In one embodiment, theidentifications for the user (101) may be additionally or alternativelybased on transaction profiles associated with others. Therecommendations may be determined by predictive association and otheranalytical techniques.

For example, the identifications for the user (101) may be based on thetransaction profile of another person. The profile selector (129) mayapply predetermined criteria to identify another person who, to apredetermined degree, is deemed sufficiently similar to the user (101).The identification of the other person may be based on a variety offactors including, for example, demographic similarity and/or purchasingpattern similarity between the user (101) and the other person. As oneexample, the common purchase of identical items or related items by theuser (101) and the other person may result in an association between theuser (101) and the other person, and a resulting determination that theuser (101) and the other person are similar. Once the other person isidentified, the transaction profile constituting the SKU-level profilefor the other person may be analyzed. Through predictive association andother modeling and analytical techniques, the historical purchasesreflected in the SKU-level profile for the other person may be employedto predict the future purchases of the user (101).

As another example, the identifications of the user (101) may be basedon the transaction profiles of a group of persons. The profile selector(129) may apply predetermined criteria to identify a multitude ofpersons who, to a predetermined degree, are deemed sufficiently similarto the user (101). The identification of the other persons may be basedon a variety of factors including, for example, demographic similarityand/or purchasing pattern similarity between the user (101) and theother persons. Once the group constituting the other persons isidentified, the transaction profile constituting the SKU-level profilefor the group may be analyzed. Through predictive association and othermodeling and analytical techniques, the historical purchases reflectedin the SKU-level profile for the group may be employed to predict thefuture purchases of the user (101).

The SKU-level profile of the user (101) may be provided to select anadvertisement that is appropriately targeted. Because the SKU-levelprofile of the user (101) may include identifications of the goods andservices that the user (101) may be likely to buy, advertisementscorresponding to the identified goods and services may be presented tothe user (101). In this way, targeted advertising for the user (101) maybe optimized. Further, advertisers and publishers of advertisements mayimprove their return on investment, and may improve their ability tocross-sell goods and services.

In one embodiment, SKU-level profiles of others who are identified to besimilar to the user (101) may be used to identify a user (101) who mayexhibit a high propensity to purchase goods and services. For example,if the SKU-level profiles of others reflect a quantity or frequency ofpurchase that is determined to satisfy a threshold, then the user (101)may also be classified or predicted to exhibit a high propensity topurchase. Accordingly, the type and frequency of advertisements thataccount for such propensity may be appropriately tailored for the user(101).

In one embodiment, the SKU-level profile of the user (101) may reflecttransactions with a particular merchant or merchants. The SKU-levelprofile of the user (101) may be provided to a business that isconsidered a peer with or similar to the particular merchant ormerchants. For example, a merchant may be considered a peer of thebusiness because the merchant offers goods and services that are similarto or related to those of the business. The SKU-level profile reflectingtransactions with peer merchants may be used by the business to betterpredict the purchasing behavior of the user (101) and to optimize thepresentation of targeted advertisements to the user (101).

Details on SKU-level profile in one embodiment are provided in U.S.patent application Ser. No. 12/899,144, filed Oct. 6, 2010, published asU.S. Pat. App. Pub. No. 2011/0093335 and entitled “Systems and Methodsfor Advertising Services Based on an SKU-Level Profile,” the disclosureof which is hereby incorporated herein by reference.

Real-Time Messages

In one embodiment, the transaction handler (103) is configured tocooperate with the media controller (115) to facilitate real-timeinteraction with the user (101) when the payment of the user (101) isbeing processed by the transaction handler (103). The real-timeinteraction provides the opportunity to impact the user experienceduring the purchase (e.g., at the time of card swipe), throughdelivering messages in real-time to a point of interaction (107), suchas a mobile phone, a personal digital assistant, a portable computer,etc. The real-time message can be delivered via short message service(SMS), email, instant messaging, or other communications protocols.

In one embodiment, the real-time message is provided without requiringmodifications to existing systems used by the merchants and/or issuers.

FIG. 9 shows a system to provide real-time messages according to oneembodiment. In FIG. 9, the transaction handler (103) (or a separatecomputing system coupled with the transaction handler (103)) is todetect the occurrence of certain transactions of interest during theprocessing of the authorization requests received from the transactionterminal (105); a message broker (201) is to identify a relevant messagefor the user (101) associated with the corresponding authorizationrequest; and the media controller (115) is to provide the message to theuser (101) at the point of interaction (107) via a communication channelseparate from the channel used by the transaction handler (103) torespond to the corresponding authorization request submitted from thetransaction terminal (105).

In one embodiment, the media controller (115) is to provide the messageto the point of interaction (107) in parallel with the transactionhandler (103) providing the response to the authorization request.

In one embodiment, the point of interaction (107) receives the messagefrom the media controller (115) in real-time with the transactionhandler (103) processing the authorization request. In one embodiment,the message is to arrive at the point of interaction (107) in thecontext of the response provided from the transaction handler (103) tothe transaction terminal (105). For example, the message is to arrive atthe point of interaction (107) substantially at the same time as theresponse to the authorization request arrives at the transactionterminal (105), or with a delay not long enough to cause the user (101)to have the impression that the message is in response to an actionother than the payment transaction. For example, the message is toarrive at the point of interaction (107) prior to the user (101)completing the transaction and leaving the transaction terminal (105),or prior to the user (101) leaving the retail location of the merchantoperating the transaction terminal (105).

In FIG. 9, the system includes a portal (143) to provide services tomerchants and/or the user (101).

For example, in one embodiment, the portal (143) allows the user (101)to register the communication reference (205) in association with theaccount data (111), such as the account information (142) of theconsumer account (146); and the media controller (115) is to use thecommunication reference (205) to deliver the message to the point ofinteraction (107). Examples of the communication reference (205) includea mobile phone number, an email address, a user identifier of an instantmessaging system, an IP address, etc.

In one embodiment, the portal (143) allows merchants and/or otherparties to define rules (203) to provide offers (186) as real-timeresponses to authorization requests; and based on the offer rules (203),the message broker (201) is to generate, or instruct the mediacontroller (115) to generate, the real-time message to provide theoffers (186) to the user (101). For example, the offer (186) may includea discount, incentive, reward, rebate, gift, or other benefit, which canbe redeemed upon the satisfaction of certain conditions required by theoffer rules (203). In one embodiment, based on the offer rules (203) themessage broker (201) configures a message by selecting the appropriatemessage template from (an) existing message(s) template(s), and insertsany relevant data (e.g., the communication reference (205)) into theselected template, then passes the configured message to the mediacontroller (115), which delivers the message to the point of interaction(107). In one embodiment, the message broker (201) (or a subsystem) isused to manage message templates along with the rules for selecting theappropriate message template from among several potential choices.

In one embodiment, the offer rules (203) include offer details,targeting rules, advertisement campaign details, profile mapping,creative mapping, qualification rules, award/notify/fulfillment rules,approvals, etc. Creative elements for offers include text, images,channels, approvals, etc.

In one embodiment, when the offer rules (203) are activated by themerchant or advertiser via the portal (143), the message broker (201) isto generate trigger records (207) for the transaction handler (103). Thetransaction handler (103) is to monitor the incoming authorizationrequests to identify requests that satisfy the conditions specified inthe trigger records (207) during the process of the authorizationrequests, and to provide the information about the identified requeststo the message broker (201) for the transmission of an appropriatereal-time message in accordance with the offer rules (203).

In one embodiment, the generation of the trigger records (207) for thetransaction handler (103) is in real-time with the merchant oradvertiser activating the offer rules (203). Thus, the offer rules (203)can be activated and used for the detection of the new authorizationrequests in real-time, while the transaction handler (103) continues toprocess the incoming authorization requests.

In one embodiment, the portal (143) provides information about thespending behaviors reflected in the transaction data (109) to assist themerchants or advertisers to target offers or advertisements. Forexample, in one embodiment, the portal (143) allows merchants to targetthe offers (186) based on transaction profiles (127). For example, theoffer rules (203) are partially based on the values in a transactionprofile (127), such as an aggregated spending profile (341). In oneembodiment, the offer rules (203) are partially based on the informationabout the last purchase of the user (101) from the merchant operatingthe transaction terminal (105) (or another merchant), and/or theinformation about the location of the user (101), such as the locationdetermined based on the location of the transaction terminal (105)and/or the location of the merchant operating the transaction terminal(105).

In one embodiment, the portal (143) provides transaction basedstatistics, such as merchant benchmarking statistics, industry/marketsegmentation, etc., to assist merchants and advertisers to identifycustomers.

Thus, the real-time messages can be used to influence customer behaviorswhile the customers are in the purchase mode.

In one embodiment, the benefit of the offers (186) can be redeemed viathe transaction handler (103). The redemption of the offer (186) may ormay not require the purchase details (e.g., SKU level purchase details).Details in one embodiment about redeeming offers (186) via thetransaction handler (103) are provided in U.S. Pat. App. Ser. No.13/113,710, filed May 23, 2011 and entitled “Systems and Methods forRedemption of Offers,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, when the authorization request for a purchaseindicates that the purchase qualifies the offer (186) for redemption ifthe purchase corresponding to the authorization request is completed,the message broker (201) is to construct a message and use the mediacontroller (115) to deliver the message in real-time with the processingof the authorization request to the point of interaction (107). Themessage informs the user (101) that when the purchase is completed, thetransaction handler (103) and/or the issuer processor (145) is toprovide the benefit of the offer (186) to the user (101) via statementcredit or some other settlement value, for example points in aregistered loyalty program, or credit at the point of sale using adigital coupon delivered to the purchaser via mobile phone.

In one embodiment, the settlement of the payment transactioncorresponding to the authorization request does not occur in real-timewith the processing of the authorization request. For example, themerchant may submit the complete purchases for settlement at the end ofthe day, or in accordance with a predetermined schedule. The settlementmay occur one or more days after the processing of the authorizationrequest.

In one embodiment, when transactions are settled, the settledtransactions are matched to the authorization requests to identifyoffers (186) that are redeemable in view of the settlement. When theoffer (186) is confirmed to be redeemable based on a record ofsuccessful settlement, the message broker (201) is to use the mediacontroller (115) to provide a message to the point of interaction (107)of the user (101), such as the mobile phone of the user (101). In oneembodiment, the message is to inform the user (101) of the benefit to beprovided as statement credits and/or to provide additional offers. Inone embodiment, the message to confirm the statement credits istransmitted in real-time with the completion of the transactionsettlement.

In one embodiment, the message broker (201) is to determine the identityof the merchant based on the information included in the authorizationrequest transmitted from the transaction terminal (105) to thetransaction handler (103). In one embodiment, the identity of themerchant is normalized to allow the application of the offer rules (203)that are merchant specific.

In one embodiment, the portal (143) is to provide data insight tomerchants and/or advertisers. For example, the portal (143) can providethe transaction profile (127) of the user (101), audience segmentationinformation, etc.

In one embodiment, the portal (143) is to allow the merchants and/oradvertisers to define and manage offers (186) for their creation,fulfillment and/or delivery in messages.

In one embodiment, the portal (143) allows the merchants and/oradvertisers to test, run and/or monitor the offers (186) for theircreation, fulfillment and/or delivery in messages.

In one embodiment, the portal (143) is to provide reports and analyticsregarding the offers (186).

In one embodiment, the portal (143) provides operation facilities, suchas onboarding, contact management, certification, file management,workflow assistance, etc. to assist the merchants and/or advertisers tocomplete the tasks related to the offers (186).

In one embodiment, the portal (143) allows the user (101) to opt in oropt out of the real-time message delivery service.

In one embodiment, an advertiser or merchant can select an offerfulfillment method from a list of options, such as statement credits,points, gift cards, e-certificates, third party fulfillment, etc.

In one embodiment, the merchant or advertiser is to use the “off therack” transaction profiles (127) available in the data warehouse (149).In one embodiment, the merchant or advertiser can further editparameters to customize the generation of the transaction profiles (127)and/or develop custom transaction profiles from scratch using the portal(143).

In one embodiment, the portal (143) provides a visualization tool toallow the user to see clusters of data based on GeoCodes, proximity,transaction volumes, spending patterns, zip codes, customers, stores,etc.

In one embodiment, the portal (143) allows the merchant or advertiser todefine cells for targeting the customers in the cells based ondate/time, profile attributes, map to offer/channel/creative, conditiontesting, etc.

In one embodiment, the portal (143) allows the merchant or advertiser tomonitor the system health, such as the condition of servers, filesreceived or sent, errors, status, etc., the throughput by date or range,by program, by campaign, or by global view, and aspects of currentprograms/offers/campaigns, such as offer details, package audit reports,etc. In one embodiment, reporting includes analytics and metrics, suchas lift, conversion, category differentials (e.g., spending patterns,transaction volumes, peer groups), and reporting by program, campaign,cell, GeoCode, proximity, ad-hoc, auditing, etc.

FIG. 10 shows a method to provide real-time messages according to oneembodiment. In FIG. 10, a computing apparatus is to generate (211) atrigger record (207) for a transaction handler (103) to identify anauthorization request that satisfies the conditions specified in thetrigger record (207), receive (213) from the transaction handler (103)information about the authorization request in real-time with thetransaction handler (103) providing a response to the authorizationrequest to a transaction terminal (105), identify (215) a communicationreference (205) of a user (101) associated with the authorizationrequest, determine (217) a message for the user (101) responsive to theauthorization request, and provide (219) the message to the user (101)at a point of interaction (107) via the communication reference (205),in parallel with the response from the transaction handler (103) to thetransaction terminal (105).

In one embodiment, the computing apparatus includes at least one of: atransaction handler (103), a message broker (201), a media controller(115), a portal (143) and a data warehouse (149).

Trigger Rank

FIG. 11 shows a system to arrange the delivery of real-time messagesaccording to one embodiment. In FIG. 11, the portal (143) is configuredto provide a marketer (241) with a user interface to set up a messagecampaign related to an offer (186), such as a message campaign toprovide the offer (186) to users (e.g., 101) under certain conditions,to modify the offer (186) under other conditions, and to providereminders of the offer (186) under further conditions.

In one embodiment, the messages transmitted in the message campaign arein response to transactions made by the respective users (e.g., 101) andin response to the respective transactions being processed by thetransaction handler (103). In one embodiment, the messages aretransmitted to the users (e.g., 101) in real time and/or in parallelwith the processing and transmitting of the authorization responses forthe respective transactions.

In one embodiment, the data warehouse (149) is configured to storetrigger records (207), each of which specifies a set of conditions thata transaction needs to meet to trigger an action by the message broker(201). The transaction handler (103) is configured to monitor thetransactions processed at the transaction handler (103) to detecttransactions that satisfy the trigger records (207); and in response tothe transaction handler (103) detecting a transaction that satisfies theconditions specified in a trigger record (207), the transaction handler(103) is configured to cause the message broker (201) to generate andtransmit a message in accordance with the respective trigger record(207). The message may provide the offer (186), modify the offer (186),reminder the user (101) of the offer (186), etc.

In one embodiment, the portal (143) of the real-time messaging systemillustrated in FIG. 11 provides marketers with user interfaces to set up“triggers” corresponding to the trigger records (207). For example, atrigger may correspond to the condition of “spending $X at Y Merchant byan enrollee;” and an action may be associated with the trigger toprovide an offer (e.g., 186) to the corresponding enrollee.

In one embodiment, the portal (143) is configured to help marketers setup triggers for their messages, advertisements and/or offers, throughautomatically ranking triggers, selecting valuable and/or effectivetriggers, and/or filtering out ineffective triggers.

In general, there would be thousands of potential triggers that could beused for targeting offers or advertisements. However, there might beonly a smaller number of triggers that are valuable and/or effective incausing desired actions of the enrollees, such as making purchasesaccording to the advertisements or taking advantage of the benefit ofthe offers. In one embodiment, the portal (143) is configured to rankthe triggers according to their potential to increase revenue via theoffer (e.g., 186) delivered via the respective triggers.

In one embodiment, the portal (143) allows a marketer to log in andspecify offers (186) and rules or triggers for the delivery of theoffers (186) and/or messages related to the offers (186), such asreminders, notifications, modifications, etc.

For example, in one embodiment, the portal (143) provides a userinterface to receive from the marketer (241) inputs to define the offer(186), user segment identification (243) that includes characteristicsused to identify a set of users (101) to be targeted with the offer(186), and trigger identification (245) that specifies the triggerconditions for each message that is related to the offer (186) andscheduled to be transmitted to the respective users (101) in response totransactions satisfying the trigger conditions.

Once the offers and triggers are specified in a self-service way usingthe portal (143), the system as illustrated in FIG. 11 is configured todeliver the messages containing the offers (186) and/or related to theoffers (186) according to the input specified by the marketer (241) inthe portal (143).

In one embodiment, the system is configured to further monitorperformance of the offers/messages delivered via the triggers, viaidentifying the purchases that use the offers (and thus close the loopbetween the offer presented via the real-time messages and the purchasesthat may occur out of the context of the real-time messages, such asoffline purchases).

Examples and details regarding closing the loop, in one embodiment,between the offers presented and the respective purchases resulting fromthe offers are provided in the section entitled “CLOSE THE LOOP.”

In one embodiment, the data warehouse (149) stores data indicating thetrigger performance (251), based on correlating the messages transmittedvia predefined triggers (249) and the responses by the user (101)receiving the messages triggered by the respective predefined triggers.The trigger performance (251) is indicative of how likely a user (101)is to respond to a message if the message is received by the user (101)via the respective predefined trigger (249).

In one embodiment, the portal (143) allows the marketer (241) toidentify a targeting objective and obtain a report of ranked and/orsuggested triggers (247) that can be effective for the targetingobjective. Such a report helps the marketer (241) select triggers forthe targeting objective.

For example, in one embodiment, the marketer (241) may identify thetargeting objective as providing offers (186) to a set of enrollees tocause the enrollees to take advantage of the benefit of the offers (186)by making the purchases required for the fulfillment of the offers(186). The set of enrollees can be specified via a set of conditions forfiltering enrollees. For example, a marketer for Marriott hotels mayidentify the business objective of reaching a subset of enrollees, whoare frequent travelers and who spend more than $1000 a year on hotels.Other examples of enrollee sets include: “low share of wallet customersfor GAP,” “lapsed customers for Olive Garden,” etc.

In one embodiment, the portal (143) provides a set of filters (e.g.,user segment identification (243)) that can be applied by the marketer(241) to identify the target set of users (101). In one embodiment, someof the filters are based on the identification of the user segment orcluster, the date or time period of the spending, the merchant geographyor location of the spending, distance to merchant, correlated spending,spending amount or type, user history, and/or user attributes such asvalues (342-347) in aggregated spending profiles (e.g., 341), addresses,account statuses (e.g., lapsed, lost, new, regular), etc.

In one embodiment, after the target set of users (101) is identified viaapplying one or more filters and selectors, the portal (143) isconfigured to use the transaction data (109) to rank possible triggersfor the delivery of offers (186) to the target set of users (101). Someof the triggers may be predefined; some of the triggers may beidentified by the portal (143) automatically based on a database ofavailable parameters and rules for automated construction of triggers;and some of the triggers may be customized by the marketer (241).

In one embodiment, the transaction data (109) and/or past responses tosimilar offers are used to predict the effectiveness of the offersdelivered in accordance with the respective triggers; and the triggersare sorted or ranked according to the predicted effectiveness of therespective triggers.

In one embodiment, the transaction data (109) of the users (101)targeted by the user segment identification (243) is used to determinethe likelihood of the users (101) performing transactions that wouldsatisfy the conditions of the respective triggers. Based on the pasttransaction histories of the users (101) identified by the user segmentidentification (243), the portal (143) is configured to determine theprobability and frequencies of the users (101) performing transactionsthat satisfy the triggers identified by the trigger identification (245)and the average amounts of the transactions that satisfy the respectivetriggers. The probabilities, the frequencies and/or the averages areused to rank the triggers (e.g., based on the expected revenue impact onthe messages transmitted in accordance with the respective triggers). Inone embodiment, the trigger performance (251) is also used indetermining the expected revenue impact on the messages transmitted inaccordance with the respective triggers.

In one embodiment, the marketer (241) may specify the target set ofusers (101) via the desired purchase type. For example, the marketer forMarriott hotels may identify that the goal of the offers (186) is todrive purchases for hotel stays. The portal (143) is configured toperform an analysis to identify variables or factors that can be used tospecify the target set of users (101) that is likely to take the offers(186). For example, the portal (143) is configured to determine, basedon the transaction data (109), that airline purchases correlate to hotelstays. Thus, the marketer for Marriott hotels may use variables orfactors related to airline purchases to identify the target set of users(101). In one embodiment, the portal (143) is configured to provide areport of ranked parameters, factors, or purchases based on theircorrelation to the desired purchase type indicated by the marketer (241)(or identified based on the offer specified by the marketer (241)).

In one embodiment, once the marketer (241) specifies the desiredpurchase type, the portal (143) is configured to automatically determinea target set of users (101), based on the spending patterns reflected inthe transaction data (109). For example, the portal (143) is configuredto perform a cluster analysis and/or a factor analysis to identify theparameters to define the target set of users (101). In one embodiment,the set of parameters is presented to the marketer (241) to allowmodifications and/or customization, based on the personal knowledge ofthe marketer (241).

In one embodiment, the target set of users (101) is specified based onthe factor values of spending profiles (e.g., 341) of the respectiveusers. Alternatively or in combination, other variables, such asmarketer-defined variables based on certain predefined variablesprovided in the system, can be used to define the target set of users(101).

Since the portal (143) identifies the variables or factors relevant tothe desired transactions based on correlation relationships in thetransaction data (109), the portal (143) can discover variables orfactors that are non-obvious to typical marketers and eliminatevariables or factors that might be incorrectly perceived to have strongcorrelation with the desired transactions. Thus, the marketer (241) canuse the variables or factors to more effectively specify the target setof users (101).

In one embodiment, based on the transaction data (109) of the target setof users (101), the portal (143) is configured to provide a reportshowing a ranked list of possible triggers (247) that might be used toreach the target set of users (101). In one embodiment, based on thedesired transactions, the portal (143) is configured to automaticallyidentify the target set of users (101) and then identify the ranked listof possible triggers (247).

In one embodiment, the portal (143) is configured to rank the triggers(247) based on the probability of transactions satisfying acorresponding trigger, anticipated number of trigger events, andanticipated spending caused by the advertisement. For example, in oneembodiment, trigger ranking is based on a ranking parameter determinedvia computing the probability of a trigger occurring on the same day (orwithin a range of hours in the same day) as spending at the targetmerchant by enrollees (or accounts similar to enrollees if the data issparse) within a given time period, and multiplying the probability bythe number of trigger events anticipated (or the correlated spending atthe target merchant). Such a ranking parameter allows the sorting ofpossible triggers (e.g., in a descending order). Thus, the marketer(241) could look at the ranked triggers (247) that appear to have thehighest potential to boost incremental revenue first. In one embodiment,the portal (143) is configured to estimate the performance levels of thetriggers (247) based on transaction data (109) and rank the triggers(247) based on the estimated performance levels. The subsequentpurchases generated from the offers (186) provided according to thetriggers (247) can be used to improve the estimates and/or to determinethe actual performance level of the triggers (247). In one embodiment,the portal (143) is to automatically select the top ranking triggers(247) and adjust the selections based on the actual performance of theselected triggers (247).

In one embodiment, the portal (143) provides an auto-trigger option.When the auto-trigger option is selected, the portal (143) is configuredto automatically identify, from the transaction data (109), the purchaseevents that occur around the time that target merchant purchases occurand automatically generate triggers based on the identified purchaseevents. Thus, the marketer (241) does not need to guess what thetriggers might be or run a large number of reports to develop theirknowledge.

In one embodiment, the message broker (201) is configured to useselected enrollees' past behavior to find key factors and weights todecide whether an eligible enrollee should be provided with an offer(186) (or anything else). In one embodiment, an analysis to determinethe factors/parameters and weights is performed on an individualenrollee basis to allow customized targeting.

For example, a representative of a merchant (e.g., GAP) identifies tothe portal (143) the desire to target enrollees who are “high retailclothing spenders” as a business objective to drive purchases with a“spend $100 get $25 off” offer. Without much time to do precisetargeting, the representative can select an “Auto-Target” operation inthe self-service portal (143) for offer campaign management. In oneembodiment, the representative of the merchant defines the “high retailclothing spenders” as enrollees who are in the top 2 deciles for apredetermined spending model named “Retail Clothing Generic Model.” Therepresentative of the merchant uses the portal (143) to set up the offerparameters and submit the request to start the offer campaign. A highretail clothing spender enrolls in the service of the system illustratedin FIG. 11 via an enrollment page (e.g., via GAP's website, the issuer'swebsite, or the portal (143)) and gives permission to be marketed to bythe system. Since the enrollee is in the top 2 deciles, a set of factorsand weights customized based on the transaction data of the enrollee iscreated and loaded for the trigger. The system also automaticallyselects the time window for correlated purchases (like “within 1 day” oreven “within 3 hours”). If the enrollee makes a triggering purchase at amerchant correlated with spending at the target merchant by the enrollee(e.g., they performed this sequence before) (and if the enrollee has notbeen flagged as a control group member), an offer is sent via SMS with apurchase code that can be entered into the point of sale (POS) system atany store of the merchant to claim the benefit of the offer (186). Theenrollee may or may not respond to the offer (186) by making a purchaseto take advantage of the offer (186). The system tracks the purchasesthat result from the offers (186); and the representative of themerchant can use the portal (143) to obtain an offer performance reportto see how the offer (186) performed.

In one embodiment, the portal (143) allows the representative of themerchant to specify selection/targeting criteria for the portal (143) toselect a subset of enrollees; and the portal (143) is configured to runthe offer campaign, without revealing the identity of the targetedenrollees to the merchant before the enrollees take advantage of theoffers (186).

In one embodiment, when in the auto-trigger mode, the system isconfigured to rank triggers in a way similar to ranking the triggers togenerate the report for the marketer (241). In one embodiment, themarketer (241) may specify a few parameters, such as answers toquestions like “What time of day are you targeting?” “Does correlatedspending mean spending in the same day?” etc. in combination with theauto-trigger selection. Based on the options selected by the marketer(241) and the ranking of relevant triggers, the system is configured togenerate a set of top ranking triggers (247) to schedule the delivery ofoffers (186) on behalf of the marketer (241).

In one embodiment, the system can inject a “random” group into the listof eligible users to measure the benefit of using the Auto-Target mode.

FIG. 12 shows a method to arrange the delivery of real-time messagesaccording to one embodiment. In FIG. 12, a computing apparatus isconfigured to receive (221) an input specifying an advertisement (e.g.,offer (186)), identify (223) a target set of users (e.g., 101), identify(225) a set of triggers for delivery of the advertisement to the targetset of users (e.g., 101), rank (227) the triggers based at least in parton transaction data (109) of the target set of users (e.g., 101) togenerate a ranked list of the triggers (247), identify (229) one or moreselected triggers (247) based on the ranked list, monitor (231)transactions at a transaction handler (103) according to the one or moreselected triggers (247), and transmit (233) the advertisement to a user(101) in the target set in response to a transaction of the user (101)being processed at the transaction handler (103) matching therequirements of a selected trigger (247).

In one embodiment, the computing apparatus is configured to identify thetarget set of users (e.g., 101) based on the user segment identification(243) specified by the marketer (241). For example, the user segmentidentification (243) may use threshold values corresponding to fields inthe aggregated spending profiles (e.g., 341) to select users (e.g., 101)based on spending pattern; and the portal (143) is configured to use theaggregated spending profiles (e.g., 341) generated by the profilegenerator (121) and the threshold values to identify the target set ofusers (e.g., 101). Alternatively or in combination, the user segmentidentification (243) may be based at least in part on the account data(111), such as the geographic location of the users (e.g., 101) and/orother demographic information, such as gender, age, etc.

In one embodiment, the computing apparatus is configured to select thetop ranked triggers in an automated way. In one embodiment, thecomputing apparatus is configured to present the ranked list of triggers(247) to the marketer (241) via a user interface of the portal (143) andallow the marketer (241) to select the one or more selected triggers(247).

In one embodiment, the triggers are defined by the marketer (241) viathe trigger identification (245) that specifies the trigger conditionsof the respective triggers. In one embodiment, the triggers arepredefined by the portal (143).

In one embodiment, the triggers (247) are ranked based on a likelihoodof transactions of the users (e.g., 101) in the set satisfyingconditions specified in respective triggers (247), estimated spendingamounts for transactions satisfying conditions specified in respectivetriggers (247), and/or a likelihood of the users (e.g., 101) respondingto the message.

In one embodiment, the advertisement transmitted in response to atransaction satisfying a trigger record (207) relates to an offer of abenefit; and the transaction handler (103) is configured to provide thebenefit via statement credit after a fulfillment requirement of theoffer is satisfied based on transaction data (109) from the transactionhandler (103).

In one embodiment, the portal (143) is configured to enroll users (101)for the reception of the offers (186). An enrolled user (101) provides acommunication reference (205), such as a mobile phone number, an emailaddress, etc., that is stored as part of the account data (111) of theenrolled user (101). The message broker (201) is configured to use thecommunication reference (205) of the enrolled user (101) to transmit thereal-time message to the point of interaction (107) of the user (101),in response to a transaction of the user (101).

In one embodiment, the computing apparatus is configured to store thetrigger records (207) in the data warehouse (149) to represent the oneor more selected triggers. The transaction handler (103) and/or themessage broker (201) is configured to check transactions being processedby the transaction handler (103) against the trigger records (207) todetermine whether the conditions specified in a trigger record (207) aresatisfied by the current transaction being processed by the transactionhandler (103) and, if satisfied, cause an action specified in thetrigger record (207) for the delivery of the message related to theadvertisement (e.g., offer (186)).

In one embodiment, the computing apparatus/system includes at least oneof: the portal (143), the message broker (201), the profile generator(121), the media controller (115), the transaction handler (103), andthe data warehouse (149). In one embodiment, the portal (143) includesan analytics engine to rank the triggers (247) and/or select thetriggers (247) on behalf of a marketer (241).

In one embodiment, the ranked list of triggers (247) is presented to themarketer (241) for selection. In another embodiment, the portal (143) isconfigured to select the top triggers (247) from the ranked list for themarketer (241) in an automated way; and the portal (143) may update theselection periodically based on tracked subsequent payment transactionsresulting from the advertisement.

Trigger Visualization

In one embodiment, to help marketers set up and use triggers, the portal(143) is configured to provide the marketer (241) with a user interfacefor the visualization of triggers for the transmission of the real-timemessages. In one embodiment, the graphical representations of thetriggers can be selected to cause the user interface to further displayinformation associated with the triggers, such as the messages to betransmitted, the ranking of the triggers, trigger performance (251),likelihood of transactions satisfying the conditions specified in therespective trigger, estimated frequency of the transactions satisfyingthe conditions specified in the respective trigger, expected averagetransaction amounts satisfying the conditions specified in therespective trigger, etc. The visualization of the triggers andassociated information allows the marketers (241) to prioritize,customize, and/or select triggers.

In one embodiment, visualization of the triggers is an optionaloperation. Marketers (241) using the portal (143) could skip store-levelanalyses and choose not to visualize the available triggers. However, ifused, the visualization tool allows marketers (241) to explorecandidates in a few locations to build up their intuition beforecreating and/or committing trigger rules. In one embodiment, thevisualization tool allows the marketers (241) to browse relevant data ina graphical way and provides a possibility for serendipitous learning.

FIG. 13 illustrates a graphical representation of triggers according toone embodiment. In FIG. 13, the graphical representation (260) shows thetarget merchant location (261) where the transactions resulting from theadvertisement are expected to take place. The graphical representation(260) shows merchant locations (e.g., 263) that are near the targetmerchant location (261) and that can be used to set up triggers.

For example, in one embodiment, a trigger may be defined, or set up, asa transaction at the merchant location (263), made by a user (101)enrolled in the program to receive real-time promotion messages via thesystem illustrated in FIGS. 9 and 11. The trigger may include additionalconditions that are to be met before the advertisement or offer (186) ofthe marketer (241), or a message related to the offer (186), is to besent to the user (101). Examples of the conditions include a thresholdfor the amount involved in the transaction, the time period within a dayin which the transaction is made, a requirement to purchase an item in acategory specified by the marketer (241), etc.

In one embodiment, the marketer (241) may select the merchant location(263) to view data related to the trigger set using the merchantlocation (263). For example, such data may include the correlationdegree of purchases made at the selected merchant location (263) and thepurchases made at the target merchant location (261), determined basedon past transaction data (109). For example, such data may include thecorrelation degree of the type of purchases made at the selectedmerchant location (263) and the type of purchases made at the targetmerchant location (261), determined based on past transaction data(109). For example, such data may include the population size of generalusers who have made purchases at the merchant location (263), and/or thepopulation size of enrolled users who have made purchases at themerchant location (263). For example, such data may include the spendingfrequency and/or amount by the enrolled users at the selected merchantlocation (263).

In one embodiment, the graphical representation (260) shows a ranked setof triggers at their corresponding merchant locations (e.g., 263). Forexample, in FIG. 13, the triggers are ranked in alphabetical order(e.g., A, B, . . . ) so that the marketer (241) may explore the topranking triggers first. Examples and details of ranking triggers in oneembodiment are provided in the section entitled “TRIGGER RANK.”

In one embodiment, after the message campaign starts, the portal (143)is configured to track the performance of the triggers; and thegraphical representation (260) is configured to display the triggersaccording to the performance of the triggers. The respective graphicalrepresentations of the triggers (e.g., 263) can be selected to view thestatistics about respective triggers, such as the numbers of messages oroffers transmitted in accordance with the triggers, purchasetransactions resulting from the transmission of the messages or offers,an aggregated spending profile of a group of users receiving themessages or offers via the triggers, an aggregated spending profile of agroup of users responding to the messages or offers, etc.

In one embodiment, the merchant locations and/or the triggers are rankedby the portal (143) using the transaction data (109). Thus, the topranking triggers are presented in the graphical representation (260) andthe bottom ranking triggers and/or locations are not presented to avoidcluttering. In one embodiment, the graphical representation includes aset of navigation controls that allow the marketer (241) to selectivelyview a subset of the ranked triggers (e.g., the top predetermined numberof triggers, or the next predetermined number of triggers, etc.)

In one embodiment, the portal (143) is configured to make triggerrecommendations using a location score. For example, in one embodiment,the location score is computed based on multiplying the aggregatedenrollees' spending at the merchant location (263) by the probabilitythat the spending at the merchant location (263) is followed by spendingat the target merchant location (261) within a predetermined period oftime (e.g., one hour, three hours, a day, or the same day). Thus, thegraphical representation (260) helps the marketer (243) visualize amerchant location (263) for how a trigger might work.

In one embodiment, the triggers are ranked according to the locationscore. In one embodiment, the portal (143) ranks the triggers accordingto the methods discussed in the section entitled “TRIGGER RANK.”

In one embodiment, the graphical representation (260) of FIG. 13 showsthe locations (e.g., 265) that may be used for triggers but are not inthe recommended list of trigger locations, or not currently used in thetriggers selected by the marketer (241). The graphical representation(260) allows the marketer to explore the identity of the merchants atthe locations (e.g., 265), and/or related transaction-based intelligenceinformation, such as the location score, the probability of transactionsoccurring at the location (265), the expected transaction amount at thelocation (265), the probability of a transaction at the location (265)followed by a purchase at the merchant location (261), the spendingpatterns of enrolled users who made transactions at the location (265),the spending patterns of enrolled uses who made transactions at thelocation (265) at a frequency higher than a threshold, etc.

Alternatively or in combination, the graphical representation of FIG. 13shows the locations (e.g., 265) of enrolled users and/or the locations(e.g., 265) of the target set of users (e.g., 101). The graphicalrepresentation (260) allows the marketer (241) to explore the spendingattributes of the users at the locations (e.g., 265), such as the values(342-347) in the aggregated spending profile (341) of the users (e.g.,101). The attributes can be used in formulating triggers.

The graphical representation (260) provided in FIG. 13 is intended forillustration purposes. The actual tool can show other items such ascompetitive merchant locations, show more information on the screen,allow the marketer (241) to right-click to create a trigger rule on thespot, delete a trigger, modify a trigger, view real time transactiondata related to the triggers, etc.

In one embodiment, the graphical representation (260) not only showsdata about enrollees but also data related to other users who havesimilar spending patterns as enrollees. The similarity between the userscan be identified based on the spending profiles (e.g., 341, 127, and131).

FIG. 14 illustrates a method to present triggers according to oneembodiment. In FIG. 14, a computing apparatus is configured to identify(271) a set of triggers for delivery of the advertisement to the targetset of users (e.g., 101), provide (273) a graphical representation (260)of the triggers, and provide (275) data related to the triggers on thegraphical representation (260), in response to user interaction with thegraphical representation (260).

In one embodiment, each of the triggers identifies a set of one or moreconditions which, when satisfied by a transaction of a user (101),causes the transmission of a message to the user (101) in real time asthe transaction is being processed by the transaction handler.

In one embodiment, the graphical representation (260) includes a mapshowing the locations of the triggers. In one embodiment, the locationof a trigger corresponds to the location of a merchant (e.g., 263) atwhich the transaction is to occur to satisfy the requirements of thetrigger.

In one embodiment, the computing apparatus is configured to rank thetriggers based on transaction data (109) recorded by the transactionhandler (103); and the graphical representation (260) indicates theranks of the triggers (e.g., via A, B, C, etc. in FIG. 13).

In one embodiment, the computing apparatus is configured to determine ascore for each of the plurality of triggers, based on an amount oftransactions performed at the location of a respective trigger and aprobability of a transaction at the location of the respective triggerfollowed by a transaction at a location of a merchant for which themessage is to be transmitted. In one embodiment, the triggers are rankedbased on the score.

In one embodiment, the computing apparatus is configured to provide auser interface to receive user identification information from amarketer (241) and identify a plurality of users (e.g., 101) who satisfythe user identification information. The message is specified to betransmitted to the plurality of users (e.g., 101) according to theplurality of triggers. In one embodiment, the message is not transmittedto users who are not among the plurality of users who satisfy the useridentification information.

In one embodiment, the computing apparatus is configured to receive aselection of a first trigger from the graphical representation (260) ofthe plurality of triggers and provide data related to the first triggeron the graphical representation (260), in response to the selection ofthe first trigger.

For example, in one embodiment, the data provided in response to theselection of the first trigger includes: an indication of a degree ofrelevancy between a transaction desired by the message and a transactionsatisfying the condition of the first trigger, an indication of anoccurrence frequency of transactions satisfying the first trigger, anindication of a population size of users eligible for the message to betransmitted in accordance with the first trigger, and/or an indicationof spending amounts of users eligible for the message to be transmittedin accordance with the first trigger.

In one embodiment, the graphical representation (260) includes at leastone first location (e.g., 265) which when selected causes a presentationof a user interface to set up a trigger at the first location (e.g.,265).

In one embodiment, the graphical representation (260) includes at leastone candidate for a first trigger at a first location (265), which whenselected causes a presentation of support data for setting up the firsttrigger at the first location (265). For example, in one embodiment, thesupport data includes at least one of: a probability of transactionsoccurring at the first location (265); a probability of a transaction atthe first location (265) followed by a transaction at a location of amerchant for which the message is to be transmitted; and a spendingpattern of users who have made transactions at the first location (265).

In one embodiment, the spending pattern of users who have madetransactions at the first location (265) is based on one or more fieldsof an aggregated spending profile (341) of the users.

In one embodiment, the graphical representation (260) includes furtherinformation such as indication of performances of the plurality oftriggers, numbers of potential users associated with the triggers, etc.

In one embodiment, the triggers are represented on the graphicalrepresentation (260) via icons (e.g., 263). The icons can be furtherselected to explore information associated with the respective triggers,such as the message to be transmitted when a transaction satisfying theconditions of the trigger is detected, at least one condition of atransaction occurring in a location of the trigger, performanceinformation of the first trigger, opportunity information for using thefirst trigger, etc.

In one embodiment, the performance information includes the locationscore of the trigger, the rank of the trigger, statistics of pasttransactions that are generated as a result of the message transmittedin accordance with the trigger, and/or statistics of past transactionsthat would satisfy the requirements of the trigger.

In one embodiment, the opportunity information includes the number ofenrolled users (101) who have made transactions in the past at thelocation of the trigger, the spending patterns of such users (101), theaggregated spending profile of such users (101) as a group, the spendingamount and/or frequency of such users (101) in a merchant categoryrelevant to the marketer (241), etc.

In one embodiment, the computing apparatus/system includes at least oneof: the transaction handler (103) to process the transactions and thedata warehouse (149) coupled with the transaction handler to storetransaction data (109) recording the transactions, the portal (143)coupled with the transaction handler (103) and the data warehouse (149),the message broker (201) coupled with the transaction handler (103) andthe data warehouse (149) to generate and transmit the message using acommunication reference (205) of the user (101), the media controller(115), and the profile generator (121).

In one embodiment, the portal (143) is configured to receive first databased on which a set of users is identified, and receive second databased on which a plurality of triggers are identified, and present theplurality of triggers on a map showing a location (261) of a merchant onbehalf of which the message is to be transmitted. The message mayinclude an advertisement, an offer (186), a reminder of the offer (186),etc.

In one embodiment, the portal (143) includes an analytical engine thatranks triggers and/or merchant locations (e.g., 263, 265) for thegraphical representation (260).

Cooperative Database

In one embodiment, a cooperative database is configured to organizeintelligence information that can be used to target the delivery ofoffers by different merchants. In one embodiment, the intelligenceinformation is organized in the cooperative database according tocommunities. A merchant of a community is provided with the facility toselectively share the intelligence information associated with themerchant with other merchants.

In one embodiment, the intelligence information includes theidentification of users (101) who are enrolled to receive offers (186)via the message broker (201). For example, cardholders, such as the user(101), may register in a program to receive offers (186), such aspromotions, discounts, sweepstakes, reward points, direct mail coupons,email coupons, etc. The cardholders may register with issuers, or withthe portal (143) of the transaction handler (103), or with participatingmerchants.

For example, in one embodiment, a cardholder may enroll in a programassociated with a first merchant but not in a program associated with asecond merchant. The cooperative database allows the first merchant toselectively share with the second merchant the information about theenrollment in the program associated with the first merchant to allowthe second merchant to target offer delivery based on the enrollment inthe program associated with the first merchant.

In one embodiment, the cardholders (e.g., user (101)) whose paymenttransactions are processed by the transaction handler (103) enroll withtheir respective issuers. The issuers may provide incentives tocardholders or enrollees to encourage enrollments. The issuers may setup limitations on allowable marketing activities, based on marketingobjectives of the issuers and/or knowledge about the needs and concernsof the enrollees to protect the interest and/or privacy of theenrollees. The cardholders enrolled by the issuers are then presentedvia the portal (143) of the transaction handler (103) for marketingarrangements. For example, in one embodiment, the issuer-enrolledcardholders are assigned to one or more clusters based on the spendingbehaviors reflected in their aggregated spending profiles (e.g., 341).Merchants may target offers according to the spending behaviorsreflected in the aggregated spending profiles (e.g., 341).

In one embodiment, the cardholders may enroll in the program viamerchants, in a way similar to enrolling via issuers.

In one embodiment, the cardholders may directly enroll in the programvia the portal (143), without having to enroll through respectiveissuers.

FIG. 15 illustrates a system to use a cooperative database to targetoffers according to one embodiment. In FIG. 15, the data warehouse (149)organizes enrollees into communities (e.g., 401, 402, etc.).

In one embodiment, the assignment of enrollees in the communities (e.g.,401, 402, etc.) is not exclusive. For example, an enrollee may be inmultiple communities. Alternatively, each enrollee is to be assigned toonly one community in one embodiment.

In one embodiment, a community (e.g., 401) is to be associated with oneor more entities such as merchants, issuers, acquirers, etc. Suchentities in the community (e.g., 401) agree to share information aboutthe identification of the enrollees in the community (e.g., 401) for thepurpose of targeting offers to the enrollees in the community (e.g.,401), and may set up restrictions and/or permissions on marketing accessto enrollees by merchants associated with other communities.

In one embodiment, the identification of the enrollees in a community(e.g., 401) is based on information related to and/or received from themerchants in the community (e.g., 401). When a user (101) is identifiedvia information related to and/or received from a first merchant, theidentification of the user (101) is generally limited to being used bythe first merchant. Through the use of the cooperative dataset, thefirst merchant can selectively share the identification of the user(101) with other second merchants, which may also share theidentification of users made via information related to and/or receivedby the second merchants with the first merchant via the communityarrangement.

In one embodiment, the enrollees in the community (e.g., 401) are thecustomers of the merchants in the community (e.g., 401). The enrolleesmay be enrolled by the merchants (e.g., via a loyalty program). In oneembodiment, the merchants are represented by respective acquirers in theenrollment process. For example, a set of merchants, issuers and/oracquirers may set up a community (e.g., 401) and enroll users (e.g.,101) in the community (e.g., 401) for receiving a type of offers. In oneembodiment, one or more issuers or the portal (143) associated with thetransaction handler (103) are used to enroll the cardholders forreal-time delivery of offers (186).

In one embodiment, the enrollees are enrolled via the portal (143); andthe enrollees are assigned to the community (e.g., 401) based on thetransaction data (109). For example, in one embodiment, when theenrollees have a spending pattern with the one or more merchants in thecommunity (e.g., 401), the enrollees are assigned to the community(e.g., 401); for example, in one embodiment, when the enrollees havespent more than a threshold amount with the merchants in the community(e.g., 401), the enrollees are assigned to the community (e.g., 401);for example, in one embodiment, when the enrollees have recently shoppedwith the merchants in the community (e.g., 401), the enrollees areassigned to the community (e.g., 401). The identification of theassociation of an enrollee and the community (401) provides informationon the spending behavior of the enrollee in connection with the businessentities in the community (401). The portal (143) provides a userinterface to allow the respective business entities, such as merchants,issuers, acquirers, etc., to set up access control to protect theavailability of the identification information and to selectively allowmarketing to the enrollees identified via the community (401).

In one embodiment, the identification of the communities (e.g., 401-402)can be used by merchants to identify the target set of enrollees foroffer targeting and/or set up triggers for the delivery of the offers.

For example, in one embodiment, the enrollees in community A (401) arecustomers of one or more merchants. The merchants may provide data(e.g., 403) allowing certain merchants to market to the enrollees viathe community A (401) and disallowing other merchants to market to theenrollees via the community A (401).

For example, in one embodiment, the enrollees in community A (401) areenrolled via one or more issuers; and the issuers may set up limitations(e.g., 405) on allowable marketing activities, based on marketingobjectives of the issuers, and the interests and concerns of theenrollees.

In one embodiment, the portal (143) of the transaction handler (103)provides an interface to assist merchants to formulate rules (203) totarget offers (186). For example, the merchants may use the portal (143)to explore the information regarding potential customers, based on thespending behavior patterns extracted from the transaction data (109)and/or other information, such as relevant merchants, nearby merchants,etc.

Based on the spending behavior patterns and/or other parameters, themerchants may apply various filters to select a target set of enrolleesfor targeting offers.

In one embodiment, the portal (143) of the transaction handler (103)provides a user interface to allow the merchants to set up triggers forthe delivery of their offers. The analytical engine (415) is to providethe intelligence information based at least in part on the past paymenttransactions, which allows the merchants to gain data insights forcampaign targeting. For example, in one embodiment, at least part of theintelligence information can be presented via the trigger visualizationtechnique described in the section entitled “TRIGGER VISUALIZATION.”

In one embodiment, the portal (143) is configured to limit the targetingof offers based on the partner data (e.g., 403-406) in the communities(e.g., 401-402). For example, based on the partner data (e.g., 403-406),the portal (143) is to determine whether a particular merchant isentitled to select enrollees based on the identification of a community(e.g., 401).

For example, in one embodiment, when permitted by the partner data (403,405, . . . ) of the community A (401), a merchant of community B (402)may choose to select the enrollees of the community A (401) as thetarget set of users for receiving an offer (186) from the merchant ofcommunity B (402). In one embodiment, the merchant of community B (402)can further apply filters, based on spending patterns and/or otherconditions, to select a subset of the enrollees from the community A(401) to form the target set of users. In one embodiment, the merchantof community B (402) may select multiple subsets from one or morecommunities to form the target set of users.

Thus, the system allows the merchants and/or other entities such asissuers and/or acquirers to set up rules to allow the sharing ofinformation related to the relationships between enrollees and thebusiness entities and to allow collaboration in targeting offers.

After the trigger records (207) are determined using the self-serviceinterface provided to the merchants via the portal (143), the eventdetection engine (413) monitors the transaction data (109) in real-timewith the processing of the respective transactions to detect events thatsatisfy the trigger records (207). In response to events satisfying thetrigger records (207), the message broker (201) is to use the mediacontroller (115) to transmit messages to the point of interaction (107)of the respective enrollees, in real-time with the processing of thecorresponding triggering transaction by the transaction handler (103).For example, the messages can be transmitted via SMS, email, or socialmedia in accordance with the communication reference (205) of anenrollee, such as the user (101).

In FIG. 15, the system includes an analytical engine (415) and a patternrecognition engine (411). In one embodiment, a triggering event isidentified based on a transaction pattern; and the last transaction thatcauses the detection of the pattern by the pattern recognition engine(411) is used as a triggering transaction; and the real-time message istransmitted to the enrollee who made the payment through the triggeringtransaction. In one embodiment, a trigger is detected based onfrequency, recency, payment amount, propensity to redeem, and/or apattern of multiple transactions.

In one embodiment, the message broker (201) is configured to generatenotification messages when the enrollees make the transactions thatsatisfy the redemption requirements of the offers (186). The transactionhandler (103) is configured to provide the benefits of the offers (186)via statement credits in one embodiment.

In one embodiment, the correlator (117) is configured to correlate theoffers (186) with the purchases that redeem the offers (186) to measurethe performance of the offers (186). The portal (143) is configured toprovide a user interface for the merchants to request a performancereport for the offers (186).

In one embodiment, the advertisement selector (133) is configured to usevarious information about an enrollee to build a model for determiningthe relevancy of an offer (186) relative to an opportunity to providethe offer (186) to the enrollee (e.g., user (101)).

In one embodiment, advertisers are allowed to place bids for thetargeting opportunity via the portal (143); and the message broker (201)is configured to use the bids and the relevance determined by the modelto select an offer from a plurality of competing offers (186). In oneembodiment, the analytical engine (415) includes a learning module toimprove the evaluation of the relevancy based on the tracked performanceof the offers (186).

In one embodiment, a trigger is set up to transmit an offer (186) from amerchant when an enrollee, such as the user (101), is shopping with themerchant. In one embodiment, the offer (186) is sent to entice theenrollee to come back to the merchant sooner than normal. For example,the offer (186) may provide a discount valid for a short period of time(e.g., one week). In some embodiments, based on the cooperativedatabase, the offer (186) provides a discount for a purchase at across-over merchant and/or for complimentary products or services.

In one embodiment, a trigger is set up to transmit an offer (186) from amerchant when an enrollee, such as the user (101), is shopping near themerchant. In one embodiment, the offer (186) is sent to entice theenrollee to come to the merchant. For example, the offer (186) mayprovide a discount valid for a short period of time (e.g., 2 hours); andthe offer (186) may be sent with the information about the nearestlocations of the merchant. For example, in accordance with the rules inthe cooperative database, an offer (186) from a first merchant may besent to an enrollee when the enrollee makes a purchase from a secondmerchant. The offer (186) may provide product information and/or adiscount, reward, gift, incentive, or statement credit. In oneembodiment, an offer (186) is sent when the enrollee makes a paymenttransaction at a peer set merchant category.

In one embodiment, the analytical engine (415) is configured to predictwhether an enrollee plans to be near a merchant. If the enrollee plansto be near a merchant based on the prediction, an offer (186) valid forthe next period of time (e.g., a few hours) is transmitted to theenrollee.

In one embodiment, the pattern recognition engine (411) is configured todetermine the spending pattern of the enrollee, such as the typicalspending amount with the merchant; and the offer (186) provides adiscount when the enrollee spends an amount more than the typicalspending amount.

In one embodiment, the offer (186) is sent based on a spending frequency(e.g., one discount offer transmitted every 5^(th) time of theoccurrence of a recognized transaction pattern).

In one embodiment, the partner data (403) associated with a partnermerchant in the community (401) allows a merchant in the community torequest an offer (186) of the merchant to be transmitted to an enrolleewhen the enrollee makes a purchase from the merchant. In one embodiment,an offer (186) of a merchant in the community (401) is configured to beprovided to an enrollee when the enrollee makes a purchase (from aseparate partner merchant) that has a high correlation or affinity inrelation to purchases at the merchant. In one embodiment, when theenrollee makes a purchase in a peer set merchant category, the messagebroker (201) is to identify from the peer set merchant category amerchant that has the highest affinity relation to the purchase andtransmit an offer (186) from the identified merchant to the enrollee.

In one embodiment, the analytical engine (415) is configured to predictwhat an enrollee needs based on the purchase history of the enrollee. Anoffer (186) from the merchant who provides products or services thatmeet the predicted needs of the enrollee is sent to the enrollee inresponse to the prediction. In one embodiment, the offer (186) mayprovide a discount on items not commonly purchased. In one embodiment,the offer (186) may provide a discount when the enrollee spends morethan the typical spending amount. The offer (186) may be configured tobe valid for a time period that ends before the time predicted for thenext purchase of the enrollee.

In one embodiment, the analytical engine (415) and/or the patternrecognition engine (411) is configured to detect the change in spendingbehavior of an enrollee. An offer (186) from the merchant is configuredto be sent in response to a detected change in spending behavior. Forexample, in one embodiment, an offer (186) is provided to an enrolleewhen the enrollee is purchasing less often. In one embodiment, anotification is provided to the enrollee when there is a change in thezip code (347) of the dominant geographic area in which the spendingassociated with the enrollee occurs. In one embodiment, an offer (186)is provided to an enrollee when the purchases made by the enrollee areless valuable than previous purchases.

In one embodiment, an offer (186) is transmitted to an enrollee when theactivities of the enrollee in a loyalty program decrease.

FIG. 16 illustrates a method to target offers using a cooperativedatabase according to one embodiment. In one embodiment, a computingapparatus is configured to store (281) transaction data (109) recordingtransactions processed by a transaction handler (103), organize (283)entities (e.g., merchants, issuers, acquires, users (101)) according tocommunity, receive (285) a request from a merchant in a second community(402) but not in a first community (401), and present (287) an offer(186) of the merchant in the second community (402) to users (101)identified via the transaction data (109) and first data (e.g., 403) inthe first community (401).

In one embodiment, the first data (e.g., 403) is received from aseparate merchant in the first community (401). In one embodiment, thefirst data (e.g., 403) includes enrollment data. In one embodiment, thefirst data (e.g., 403) provides permission for the merchant in thesecond community (402) to use the relationship between the users and themerchants in the first community (401) to identify the users (101).

In one embodiment, the computing apparatus is configured to organizethird party data according to community, where the third party dataincludes the first data (e.g., 403, 405) received from a first pluralityof entities of the first community (401) and second data (e.g., 404,406) received from a second plurality of entities of a second community(402). According to a request from the merchant in the second community(402), the computing apparatus presents an offer of the merchant in thesecond community (402) to users (e.g., 101) identified via thetransaction data (109) and the first data (e.g., 403) received from thefirst plurality of entities of the first community (401). In oneembodiment, the merchant in the second community (402) is not in thefirst community (401).

In one embodiment, the first data (403) includes limitations on accessto the first data by entities outside the first community (401); and theusers are identified in accordance with the limitations. In oneembodiment, the limitations are specified by the entities in the firstcommunity (401) via the portal (143) of the transaction handler (103).In one embodiment, the limitations are specified by a merchant, anissuer, or an acquirer in the first community (401).

In one embodiment, the request from the merchant in the second community(402) specifies that the users be identified as customers of a merchantin the first community (401) who have not made purchases from themerchant in the second community (402) in a predefined recent timeperiod (e.g., a week, a month, a quarter, a year).

In one embodiment, the computing apparatus is configured to monitorfirst transactions of the users during processing of the firsttransactions by the transaction handler (103), detect a subset of thefirst transactions satisfying presentation requirements of the offer(186) (e.g., as specified in the trigger records (207) associated withthe offer (186)), and present the offer (186) of a merchant to userscorresponding to the subset of the first transactions, in real-time withthe processing of the respective transactions in the subset by thetransaction handler (103) (e.g., after the transaction handler (103)receives the authorization requests for the respective transactions,before or shortly after the transaction handler (103) provides theauthorization responses for the respective transactions).

In one embodiment, the request from the merchant in the second community(402) specifies that the first transactions with one or more merchantsin the first community (401) be monitored for the presenting of theoffer (186). Certain first transactions with one or more merchants inthe first community (401) satisfying the trigger records (207) cantrigger the message related to the offer (186) from the merchant in thesecond community (402), in accordance with the access provided by thefirst data (e.g., 403) to the merchant in the second community (402), asspecified in the first community (401).

In one embodiment, the request from the merchant in the second community(402) specifies that users enrolled in the first community (401) bemonitored for the presenting of the offer (186). Certain actions by theenrollees in the first community (401) can trigger the message relatedto the offer (186) from the merchant in the second community (402), inaccordance with the access provided by the first data (e.g., 403) to themerchant in the second community (402), as specified in the firstcommunity (401).

In one embodiment, the offer (186) of the merchant in the secondcommunity (402) is transmitted to the users that are identified via thetransaction data and the first data (e.g., 403) via communicationreferences (205) registered during enrollment in the first community(401). Examples of the communication references (205) include phonenumber, email address and references for social media. In oneembodiment, the first data (e.g., 403) includes the communicationreferences (205) as specified during the enrollment of the accountholders in the first community (401).

In one embodiment, the computing apparatus is configured to determine aspending pattern from the transaction data (109); and the users whoreceive the offer (186) are identified further based on the spendingpattern. In one embodiment, the spending pattern includes a plurality oftransactions; and a last transaction in the spending pattern triggers apresentation of the offer (186).

In one embodiment, the computing apparatus is configured to predict atime of a next purchase of a first user (101) who is identified via thetransaction data (109) and the first data (e.g., 403) received from thefirst plurality of entities of the first community (401); and the offer(186) is provided to the first user (101) prior to the predicted time ofthe next purchase. In one embodiment, the offer (186) provided to thefirst user (101) is configured to expire prior to the predicted time ofthe next purchase.

In one embodiment, the first data (e.g., 403) includes identificationinformation of account holders (e.g., user (101)) who have enrolled inthe first community (401); and the users who are presented with theoffer (186) from the merchant in the second community (402) areidentified based at least in part on the identification information. Inone embodiment, the account holder (e.g., user (101)) is enrolled in thefirst community (401) but not the second community (402). In oneembodiment, the account holder (e.g., user (101)) is enrolled in boththe first community (401) and the second community (402). In oneembodiment, the user (101) is presented with the offer (186) from themerchant in the second community (402) at least in part as a result ofhaving enrolled in the first community (401).

In one embodiment, the computing apparatus includes one or more of: thedata warehouse (149) storing the transaction data and the data (e.g.,403-406) that are organized according to communities (e.g., 401-403),the transaction handler (103) configured to process the transactionsrecorded by the transaction data (109), the portal (143) configured toreceive first data (403, 405) from a first plurality of entities of afirst community (401) and second data (404, 406) from a second pluralityof entities of a second community (402) and configured to receive arequest from a merchant in the second community (402) for an offer(186), the message broker (201) configured to generate a messagecontaining the offer (186) to users identified via the transaction data(109) and the first data (403, 405) received from the first plurality ofentities of the first community (401), the media controller (115)configured to communicate messages generated by the message broker (201)to the point of interaction (107), the pattern recognition engine (411)configured to detect spending patterns for the detection of events thatcan trigger the delivery of the messages generated by the message broker(201), the event detection engine (413) configured to detect theoccurrence of events, based on the transaction data (109), that satisfythe requirements for presentation of the offer (186), the analyticalengine (415) configured to predict the needs and future activities ofthe users (101) based on the transaction data (109), the correlator(117) configured to correlate the offers (186) with the purchases thatredeem the offers (186) to measure the performance of the offers (186),and the advertisement selector (133) configured to select the offer(186) for the respective users (101).

Some details about the system in one embodiment are provided in thesection entitled “SYSTEM,” “CENTRALIZED DATA WAREHOUSE” and “HARDWARE.”

Variations

Some embodiments use more or fewer components than those illustrated inFIGS. 1 and 4-7. For example, in one embodiment, the user specificprofile (131) is used by a search engine to prioritize search results.In one embodiment, the correlator (117) is to correlate transactionswith online activities, such as searching, web browsing, and socialnetworking, instead of or in addition to the user specific advertisementdata (119). In one embodiment, the correlator (117) is to correlatetransactions and/or spending patterns with news announcements, marketchanges, events, natural disasters, etc. In one embodiment, the data tobe correlated by the correlator with the transaction data (109) may notbe personalized via the user specific profile (131) and may not be userspecific. In one embodiment, multiple different devices are used at thepoint of interaction (107) for interaction with the user (101); and someof the devices may not be capable of receiving input from the user(101). In one embodiment, there are transaction terminals (105) toinitiate transactions for a plurality of users (101) with a plurality ofdifferent merchants. In one embodiment, the account information (142) isprovided to the transaction terminal (105) directly (e.g., via phone orInternet) without the use of the account identification device (141).

In one embodiment, at least some of the profile generator (121),correlator (117), profile selector (129), and advertisement selector(133) are controlled by the entity that operates the transaction handler(103). In another embodiment, at least some of the profile generator(121), correlator (117), profile selector (129), and advertisementselector (133) are not controlled by the entity that operates thetransaction handler (103).

For example, in one embodiment, the entity operating the transactionhandler (103) provides the intelligence (e.g., transaction profiles(127) or the user specific profile (131)) for the selection of theadvertisement; and a third party (e.g., a web search engine, apublisher, or a retailer) may present the advertisement in a contextoutside a transaction involving the transaction handler (103) before theadvertisement results in a purchase.

For example, in one embodiment, the customer may interact with the thirdparty at the point of interaction (107); and the entity controlling thetransaction handler (103) may allow the third party to query forintelligence information (e.g., transaction profiles (127), or the userspecific profile (131)) about the customer using the user data (125),thus informing the third party of the intelligence information fortargeting the advertisements, which can be more useful, effective andcompelling to the user (101). For example, the entity operating thetransaction handler (103) may provide the intelligence informationwithout generating, identifying or selecting advertisements; and thethird party receiving the intelligence information may identify, selectand/or present advertisements.

Through the use of the transaction data (109), account data (111),correlation results (123), the context at the point of interaction,and/or other data, relevant and compelling messages or advertisementscan be selected for the customer at the points of interaction (e.g.,107) for targeted advertising. The messages or advertisements are thusdelivered at the optimal time for influencing or reinforcing brandperceptions and revenue-generating behavior. The customers receive theadvertisements in the media channels that they like and/or use mostfrequently.

In one embodiment, the transaction data (109) includes transactionamounts, the identities of the payees (e.g., merchants), and the dateand time of the transactions. The identities of the payees can becorrelated to the businesses, services, products and/or locations of thepayees. For example, the transaction handler (103) maintains a databaseof merchant data, including the merchant locations, businesses,services, products, etc. Thus, the transaction data (109) can be used todetermine the purchase behavior, pattern, preference, tendency,frequency, trend, budget and/or propensity of the customers in relationto various types of businesses, services and/or products and in relationto time.

In one embodiment, the products and/or services purchased by the user(101) are also identified by the information transmitted from themerchants or service providers. Thus, the transaction data (109) mayinclude identification of the individual products and/or services, whichallows the profile generator (121) to generate transaction profiles(127) with fine granularity or resolution. In one embodiment, thegranularity or resolution may be at a level of distinct products andservices that can be purchased (e.g., stock-keeping unit (SKU) level),or category or type of products or services, or vendor of products orservices, etc.

The profile generator (121) may consolidate transaction data for aperson having multiple accounts to derive intelligence information aboutthe person to generate a profile for the person (e.g., transactionprofiles (127), or the user specific profile (131)).

The profile generator (121) may consolidate transaction data for afamily having multiple accounts held by family members to deriveintelligence information about the family to generate a profile for thefamily (e.g., transaction profiles (127), or the user specific profile(131)).

Similarly, the profile generator (121) may consolidate transaction datafor a group of persons, after the group is identified by certaincharacteristics, such as gender, income level, geographical location orregion, preference, characteristics of past purchases (e.g., merchantcategories, purchase types), cluster, propensity, demographics, socialnetworking characteristics (e.g., relationships, preferences, activitieson social networking websites), etc. The consolidated transaction datacan be used to derive intelligence information about the group togenerate a profile for the group (e.g., transaction profiles (127), orthe user specific profile (131)).

In one embodiment, the profile generator (121) may consolidatetransaction data according to the user data (125) to generate a profilespecific to the user data (125).

Since the transaction data (109) are records and history of pastpurchases, the profile generator (121) can derive intelligenceinformation about a customer using an account, a customer using multipleaccounts, a family, a company, or other groups of customers, about whatthe targeted audience is likely to purchase in the future, howfrequently, and their likely budgets for such future purchases.Intelligence information is useful in selecting the advertisements thatare most useful, effective and compelling to the customer, thusincreasing the efficiency and effectiveness of the advertising process.

In one embodiment, the transaction data (109) are enhanced withcorrelation results (123) correlating past advertisements and purchasesthat result at least in part from the advertisements. Thus, theintelligence information can be more accurate in assisting with theselection of the advertisements. The intelligence information may notonly indicate what the audience is likely to purchase, but also howlikely the audience is to be influenced by advertisements for certainpurchases, and the relative effectiveness of different forms ofadvertisements for the audience. Thus, the advertisement selector (133)can select the advertisements to best use the opportunity to communicatewith the audience. Further, the transaction data (109) can be enhancedvia other data elements, such as program enrollment, affinity programs,redemption of reward points (or other types of offers), onlineactivities, such as web searches and web browsing, social networkinginformation, etc., based on the account data (111) and/or other data,such as non-transactional data discussed in U.S. patent application Ser.No. 12/614,603, filed Nov. 9, 2009, assigned Pub. No. 2011/0054981, andentitled “Analyzing Local Non-Transactional Data with Transactional Datain Predictive Models,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, the entity operating the transaction handler (103)provides the intelligence information in real-time as the request forthe intelligence information occurs. In other embodiments, the entityoperating the transaction handler (103) may provide the intelligenceinformation in batch mode. The intelligence information can be deliveredvia online communications (e.g., via an application programminginterface (API) on a website, or other information server), or viaphysical transportation of a computer readable media that stores thedata representing the intelligence information.

In one embodiment, the intelligence information is communicated tovarious entities in the system in a way similar to, and/or in parallelwith the information flow in the transaction system to move money. Thetransaction handler (103) routes the information in the same way itroutes the currency involved in the transactions.

In one embodiment, the portal (143) provides a user interface to allowthe user (101) to select items offered on different merchant websitesand store the selected items in a wish list for comparison, reviewing,purchasing, tracking, etc. The information collected via the wish listcan be used to improve the transaction profiles (127) and deriveintelligence on the needs of the user (101); and targeted advertisementscan be delivered to the user (101) via the wish list user interfaceprovided by the portal (143). Examples of user interface systems tomanage wish lists are provided in U.S. patent application Ser. No.12/683,802, filed Jan. 7, 2010, assigned Pub. No. 2010/0174623, andentitled “System and Method for Managing Items of Interest Selected fromOnline Merchants,” the disclosure of which is hereby incorporated hereinby reference.

Aggregated Spending Profile

In one embodiment, the characteristics of transaction patterns ofcustomers are profiled via clusters, factors, and/or categories ofpurchases. The transaction data (109) may include transaction records(301); and in one embodiment, an aggregated spending profile (341) isgenerated from the transaction records (301), in a way illustrated inFIG. 2, to summarize the spending behavior reflected in the transactionrecords (301).

In one embodiment, each of the transaction records (301) is for aparticular transaction processed by the transaction handler (103). Eachof the transaction records (301) provides information about theparticular transaction, such as the account number (302) of the consumeraccount (146) used to pay for the purchase, the date (303) (and/or time)of the transaction, the amount (304) of the transaction, the ID (305) ofthe merchant who receives the payment, the category (306) of themerchant, the channel (307) through which the purchase was made, etc.Examples of channels include online, offline in-store, via phone, etc.In one embodiment, the transaction records (301) may further include afield to identify a type of transaction, such as card-present,card-not-present, etc.

In one embodiment, a “card-present” transaction involves physicallypresenting the account identification device (141), such as a financialtransaction card, to the merchant (e.g., via swiping a credit card at aPOS terminal of a merchant); and a “card-not-present” transactioninvolves presenting the account information (142) of the consumeraccount (146) to the merchant to identify the consumer account (146)without physically presenting the account identification device (141) tothe merchant or the transaction terminal (105).

In one embodiment, certain information about the transaction can belooked up in a separate database based on other information recorded forthe transaction. For example, a database may be used to storeinformation about merchants, such as the geographical locations of themerchants, categories of the merchants, etc. Thus, the correspondingmerchant information related to a transaction can be determined usingthe merchant ID (305) recorded for the transaction.

In one embodiment, the transaction records (301) may further includedetails about the products and/or services involved in the purchase. Forexample, a list of items purchased in the transaction may be recordedtogether with the respective purchase prices of the items and/or therespective quantities of the purchased items. The products and/orservices can be identified via stock-keeping unit (SKU) numbers, orproduct category IDs. The purchase details may be stored in a separatedatabase and be looked up based on an identifier of the transaction.

When there is voluminous data representing the transaction records(301), the spending patterns reflected in the transaction records (301)can be difficult to recognize by an ordinary person.

In one embodiment, the voluminous transaction records (301) aresummarized (335) into aggregated spending profiles (e.g., 341) toconcisely present the statistical spending characteristics reflected inthe transaction records (301). The aggregated spending profile (341)uses values derived from statistical analysis to present the statisticalcharacteristics of transaction records (301) of an entity in a way easyto understand by an ordinary person.

In FIG. 2, the transaction records (301) are summarized (335) via factoranalysis (327) to condense the variables (e.g., 313, 315) and viacluster analysis (329) to segregate entities by spending patterns.

In FIG. 2, a set of variables (e.g., 311, 313, 315) are defined based onthe parameters recorded in the transaction records (301). The variables(e.g., 311, 313, and 315) are defined in a way to have meanings easilyunderstood by an ordinary person. For example, variables (311) measurethe aggregated spending in super categories; variables (313) measure thespending frequencies in various areas; and variables (315) measure thespending amounts in various areas. In one embodiment, each of the areasis identified by a merchant category (306) (e.g., as represented by amerchant category code (MCC), a North American Industry ClassificationSystem (NAICS) code, or a similarly standardized category code). Inother embodiments, an area may be identified by a product category, aSKU number, etc.

In one embodiment, a variable of a same category (e.g., frequency (313)or amount (315)) is defined to be aggregated over a set of mutuallyexclusive areas. A transaction is classified in only one of the mutuallyexclusive areas. For example, in one embodiment, the spending frequencyvariables (313) are defined for a set of mutually exclusive merchants ormerchant categories. Transactions falling with the same category areaggregated.

Examples of the spending frequency variables (313) and spending amountvariables (315) defined for various merchant categories (e.g., 306) inone embodiment are provided in U.S. patent application Ser. No.12/537,566, filed Aug. 7, 2009, published as U.S. Pat. App. Pub. No.2010/0306029 and entitled “Cardholder Clusters,” and in Prov. U.S. Pat.App. Ser. No. 61/182,806, filed Jun. 1, 2009 and entitled “CardholderClusters,” the disclosures of which applications are hereby incorporatedherein by reference.

In one embodiment, super categories (311) are defined to group thecategories (e.g., 306) used in transaction records (301). The supercategories (311) can be mutually exclusive. For example, each merchantcategory (306) is classified under only one super merchant category butnot any other super merchant categories. Since the generation of thelist of super categories typically requires deep domain knowledge aboutthe businesses of the merchants in various categories, super categories(311) are not used in one embodiment.

In one embodiment, the aggregation (317) includes the application of thedefinitions (309) for these variables (e.g., 311, 313, and 315) to thetransaction records (301) to generate the variable values (321). Thetransaction records (301) are aggregated to generate aggregatedmeasurements (e.g., variable values (321)) that are not specific to aparticular transaction, such as frequencies of purchases made withdifferent merchants or different groups of merchants, the amounts spentwith different merchants or different groups of merchants, and thenumber of unique purchases across different merchants or differentgroups of merchants, etc. The aggregation (317) can be performed for aparticular time period and for entities at various levels.

In one embodiment, the transaction records (301) are aggregatedaccording to a buying entity. The aggregation (317) can be performed ataccount level, person level, family level, company level, neighborhoodlevel, city level, region level, etc. to analyze the spending patternsacross various areas (e.g., sellers, products or services) for therespective aggregated buying entity. For example, the transactionrecords (301) for a particular account (e.g., presented by the accountnumber (302)) can be aggregated for an account level analysis. Toaggregate the transaction records (301) in account level, thetransactions with a specific merchant or merchants in a specificcategory are counted according to the variable definitions (309) for aparticular account to generate a frequency measure (e.g., 313) for theaccount relative to the specific merchant or merchant category; and thetransaction amounts (e.g., 304) with the specific merchant or thespecific category of merchants are summed for the particular account togenerate an average spending amount for the account relative to thespecific merchant or merchant category. For example, the transactionrecords (301) for a particular person having multiple accounts can beaggregated for a person level analysis, the transaction records (301)aggregated for a particular family for a family level analysis, and thetransaction records (301) for a particular business aggregated for abusiness level analysis.

The aggregation (317) can be performed for a predetermined time period,such as for the transactions occurring in the past month, in the pastthree months, in the past twelve months, etc.

In another embodiment, the transaction records (301) are aggregatedaccording to a selling entity. The spending patterns at the sellingentity across various buyers, products or services can be analyzed. Forexample, the transaction records (301) for a particular merchant havingtransactions with multiple accounts can be aggregated for a merchantlevel analysis. For example, the transaction records (301) for aparticular merchant group can be aggregated for a merchant group levelanalysis.

In one embodiment, the aggregation (317) is formed separately fordifferent types of transactions, such as transactions made online,offline, via phone, and/or “card-present” transactions vs.“card-not-present” transactions, which can be used to identify thespending pattern differences among different types of transactions.

In one embodiment, the variable values (e.g., 323, 324, . . . , 325)associated with an entity ID (322) are considered the random samples ofthe respective variables (e.g., 311, 313, 315), sampled for the instanceof an entity represented by the entity ID (322). Statistical analyses(e.g., factor analysis (327) and cluster analysis (329)) are performedto identify the patterns and correlations in the random samples.

For example, a cluster analysis (329) can identify a set of clusters andthus cluster definitions (333) (e.g., the locations of the centroids ofthe clusters). In one embodiment, each entity ID (322) is represented asa point in a mathematical space defined by the set of variables; and thevariable values (323, 324, . . . , 325) of the entity ID (322) determinethe coordinates of the point in the space and thus the location of thepoint in the space. Various points may be concentrated in variousregions; and the cluster analysis (329) is configured to formulate thepositioning of the points to drive the clustering of the points. Inother embodiments, the cluster analysis (329) can also be performedusing the techniques of Self Organizing Maps (SOM), which can identifyand show clusters of multi-dimensional data using a representation on atwo-dimensional map.

Once the cluster definitions (333) are obtained from the clusteranalysis (329), the identity of the cluster (e.g., cluster ID (343))that contains the entity ID (322) can be used to characterize spendingbehavior of the entity represented by the entity ID (322). The entitiesin the same cluster are considered to have similar spending behaviors.

Similarities and differences among the entities, such as accounts,individuals, families, etc., as represented by the entity ID (e.g., 322)and characterized by the variable values (e.g., 323, 324, . . . , 325)can be identified via the cluster analysis (329). In one embodiment,after a number of clusters of entity IDs are identified based on thepatterns of the aggregated measurements, a set of profiles can begenerated for the clusters to represent the characteristics of theclusters. Once the clusters are identified, each of the entity IDs(e.g., corresponding to an account, individual, family) can be assignedto one cluster; and the profile for the corresponding cluster may beused to represent, at least in part, the entity (e.g., account,individual, family). Alternatively, the relationship between an entity(e.g., an account, individual, family) and one or more clusters can bedetermined (e.g., based on a measurement of closeness to each cluster).Thus, the cluster related data can be used in a transaction profile (127or 341) to provide information about the behavior of the entity (e.g.,an account, an individual, a family).

In one embodiment, more than one set of cluster definitions (333) isgenerated from cluster analyses (329). For example, cluster analyses(329) may generate different sets of cluster solutions corresponding todifferent numbers of identified clusters. A set of cluster IDs (e.g.,343) can be used to summarize (335) the spending behavior of the entityrepresented by the entity ID (322), based on the typical spendingbehavior of the respective clusters. In one example, two clustersolutions are obtained; one of the cluster solutions has 17 clusters,which classify the entities in a relatively coarse manner; and the othercluster solution has 55 clusters, which classify the entities in arelative fine manner. A cardholder can be identified by the spendingbehavior of one of the 17 clusters and one of the 55 clusters in whichthe cardholder is located. Thus, the set of cluster IDs corresponding tothe set of cluster solutions provides a hierarchical identification ofan entity among clusters of different levels of resolution. The spendingbehavior of the clusters is represented by the cluster definitions(333), such as the parameters (e.g., variable values) that define thecentroids of the clusters.

In one embodiment, the random variables (e.g., 313 and 315) as definedby the definitions (309) have certain degrees of correlation and are notindependent from each other. For example, merchants of differentmerchant categories (e.g., 306) may have overlapping business, or havecertain business relationships. For example, certain products and/orservices of certain merchants have cause and effect relationships. Forexample, certain products and/or services of certain merchants aremutually exclusive to a certain degree (e.g., a purchase from onemerchant may have a level of probability to exclude the user (101) frommaking a purchase from another merchant). Such relationships may becomplex and difficult to quantify by merely inspecting the categories.Further, such relationships may shift over time as the economy changes.

In one embodiment, a factor analysis (327) is performed to reduce theredundancy and/or correlation among the variables (e.g., 313, 315). Thefactor analysis (327) identifies the definitions (331) for factors, eachof which represents a combination of the variables (e.g., 313, 315).

In one embodiment, a factor is a linear combination of a plurality ofthe aggregated measurements (e.g., variables (313, 315)) determined forvarious areas (e.g., merchants or merchant categories, products orproduct categories). Once the relationship between the factors and theaggregated measurements is determined via factor analysis, the valuesfor the factors can be determined from the linear combinations of theaggregated measurements and be used in a transaction profile (127 or341) to provide information on the behavior of the entity represented bythe entity ID (e.g., an account, an individual, a family).

Once the factor definitions (331) are obtained from the factor analysis(327), the factor definitions (331) can be applied to the variablevalues (321) to determine factor values (344) for the aggregatedspending profile (341). Since redundancy and correlation are reduced inthe factors, the number of factors is typically much smaller than thenumber of the original variables (e.g., 313, 315). Thus, the factorvalues (344) represent the concise summary of the original variables(e.g., 313, 315).

For example, there may be thousands of variables on spending frequencyand amount for different merchant categories; and the factor analysis(327) can reduce the factor number to less than one hundred (and evenless than twenty). In one example, a twelve-factor solution is obtained,which allows the use of twelve factors to combine the thousands of theoriginal variables (313, 315); and thus, the spending behavior inthousands of merchant categories can be summarized via twelve factorvalues (344). In one embodiment, each factor is combination of at leastfour variables; and a typical variable has contributions to more thanone factor.

In one example, hundreds or thousands of transaction records (301) of acardholder are converted into hundreds or thousands of variable values(321) for various merchant categories, which are summarized (335) viathe factor definitions (331) and cluster definitions (333) into twelvefactor values (344) and one or two cluster IDs (e.g., 343). Thesummarized data can be readily interpreted by a human to ascertain thespending behavior of the cardholder. A user (101) may easily specify aspending behavior requirement formulated based on the factor values(344) and the cluster IDs (e.g., to query for a segment of customers, orto request the targeting of a segment of customers). The reduced size ofthe summarized data reduces the need for data communication bandwidthfor communicating the spending behavior of the cardholder over a networkconnection and allows simplified processing and utilization of the datarepresenting the spending behavior of the cardholder.

In one embodiment, the behavior and characteristics of the clusters arestudied to identify a description of a type of representative entitiesthat are found in each of the clusters. The clusters can be named basedon the type of representative entities to allow an ordinary person toeasily understand the typical behavior of the clusters.

In one embodiment, the behavior and characteristics of the factors arealso studied to identify dominant aspects of each factor. The clusterscan be named based on the dominant aspects to allow an ordinary personto easily understand the meaning of a factor value.

In FIG. 2, an aggregated spending profile (341) for an entityrepresented by an entity ID (e.g., 322) includes the cluster ID (343)and factor values (344) determined based on the cluster definitions(333) and the factor definitions (331). The aggregated spending profile(341) may further include other statistical parameters, such asdiversity index (342), channel distribution (345), category distribution(346), zip code (347), etc., as further discussed below.

In one embodiment, the diversity index (342) may include an entropyvalue and/or a Gini coefficient, to represent the diversity of thespending by the entity represented by the entity ID (322) acrossdifferent areas (e.g., different merchant categories (e.g., 306)). Whenthe diversity index (342) indicates that the diversity of the spendingdata is under a predetermined threshold level, the variable values(e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) maybe excluded from the cluster analysis (329) and/or the factor analysis(327) due to the lack of diversity. When the diversity index (342) ofthe aggregated spending profile (341) is lower than a predeterminedthreshold, the factor values (344) and the cluster ID (343) may notaccurately represent the spending behavior of the corresponding entity.

In one embodiment, the channel distribution (345) includes a set ofpercentage values that indicate the percentages of amounts spent indifferent purchase channels, such as online, via phone, in a retailstore, etc.

In one embodiment, the category distribution (346) includes a set ofpercentage values that indicate the percentages of spending amounts indifferent super categories (311). In one embodiment, thousands ofdifferent merchant categories (e.g., 306) are represented by MerchantCategory Codes (MCC), or North American Industry Classification System(NAICS) codes in transaction records (301). These merchant categories(e.g., 306) are classified or combined into less than one hundred supercategories (or less than twenty). In one example, fourteen supercategories are defined based on domain knowledge.

In one embodiment, the aggregated spending profile (341) includes theaggregated measurements (e.g., frequency, average spending amount)determined for a set of predefined, mutually exclusive merchantcategories (e.g., super categories (311)). Each of the super merchantcategories represents a type of products or services a customer maypurchase. A transaction profile (127 or 341) may include the aggregatedmeasurements for each of the set of mutually exclusive merchantcategories. The aggregated measurements determined for the predefined,mutually exclusive merchant categories can be used in transactionprofiles (127 or 341) to provide information on the behavior of arespective entity (e.g., an account, an individual, or a family).

In one embodiment, the zip code (347) in the aggregated spending profile(341) represents the dominant geographic area in which the spendingassociated with the entity ID (322) occurred. Alternatively or incombination, the aggregated spending profile (341) may include adistribution of transaction amounts over a set of zip codes that accountfor a majority of the transactions or transaction amounts (e.g., 90%).

In one embodiment, the factor analysis (327) and cluster analysis (329)are used to summarize the spending behavior across various areas, suchas different merchants characterized by merchant category (306),different products and/or services, different consumers, etc. Theaggregated spending profile (341) may include more or fewer fields thanthose illustrated in FIG. 2. For example, in one embodiment, theaggregated spending profile (341) further includes an aggregatedspending amount for a period of time (e.g., the past twelve months); inanother embodiment, the aggregated spending profile (341) does notinclude the category distribution (346); and in a further embodiment,the aggregated spending profile (341) may include a set of distancemeasures to the centroids of the clusters. The distance measures may bedefined based on the variable values (323, 324, . . . , 325), or basedon the factor values (344). The factor values of the centroids of theclusters may be estimated based on the entity ID (e.g., 322) that isclosest to the centroid in the respective cluster.

Other variables can be used in place of, or in additional to, thevariables (311, 313, 315) illustrated in FIG. 2. For example, theaggregated spending profile (341) can be generated using variablesmeasuring shopping radius/distance from the primary address of theaccount holder to the merchant site for offline purchases. When suchvariables are used, the transaction patterns can be identified based atleast in part on clustering according to shopping radius/distance andgeographic regions. Similarly, the factor definition (331) may includethe consideration of the shopping radius/distance. For example, thetransaction records (301) may be aggregated based on the ranges ofshopping radius/distance and/or geographic regions. For example, thefactor analysis can be used to determine factors that naturally combinegeographical areas based on the correlations in the spending patterns invarious geographical areas.

In one embodiment, the aggregation (317) may involve the determinationof a deviation from a trend or pattern. For example, an account makes acertain number of purchases a week at a merchant over the past 6 months.However, in the past 2 weeks the number of purchases is less than theaverage number per week. A measurement of the deviation from the trendor pattern can be used (e.g., in a transaction profile (127 or 341) as aparameter, or in variable definitions (309) for the factor analysis(327) and/or the cluster analysis) to define the behavior of an account,an individual, a family, etc.

FIG. 3 shows a method to generate an aggregated spending profileaccording to one embodiment. In FIG. 3, computation models areestablished (351) for variables (e.g., 311, 313, and 315). In oneembodiment, the variables are defined in a way to capture certainaspects of the spending statistics, such as frequency, amount, etc.

In FIG. 3, data from related accounts are combined (353). For example,when an account number change has occurred for a cardholder in the timeperiod under analysis, the transaction records (301) under the differentaccount numbers of the same cardholder are combined under one accountnumber that represents the cardholder. For example, when the analysis isperformed at a person level (or family level, business level, socialgroup level, city level, or region level), the transaction records (301)in different accounts of the person (or family, business, social group,city or region) can be combined under one entity ID (322) thatrepresents the person (or family, business, social group, city orregion).

In one embodiment, recurrent/installment transactions are combined(355). For example, multiple monthly payments may be combined andconsidered as one single purchase.

In FIG. 3, account data are selected (357) according to a set ofcriteria related to activity, consistency, diversity, etc.

For example, when a cardholder uses a credit card solely to purchasegas, the diversity of the transactions by the cardholder is low. In sucha case, the transactions in the account of the cardholder may not bestatistically meaningful to represent the spending pattern of thecardholder in various merchant categories. Thus, in one embodiment, ifthe diversity of the transactions associated with an entity ID (322) isbelow a threshold, the variable values (e.g., 323, 324, . . . , 325)corresponding to the entity ID (322) are not used in the clusteranalysis (329) and/or the factor analysis (327). The diversity can beexamined based on the diversity index (342) (e.g., entropy or Ginicoefficient), or based on counting the different merchant categories inthe transactions associated with the entity ID (322); and when the countof different merchant categories is fewer than a threshold (e.g., 5),the transactions associated with the entity ID (322) are not used in thecluster analysis (329) and/or the factor analysis (327) due to the lackof diversity.

For example, when a cardholder uses a credit card only sporadically(e.g., when running out of cash), the limited transactions by thecardholder may not be statistically meaningful in representing thespending behavior of the cardholder. Thus, in one embodiment, when thenumbers of transactions associated with an entity ID (322) is below athreshold, the variable values (e.g., 323, 324, . . . , 325)corresponding to the entity ID (322) are not used in the clusteranalysis (329) and/or the factor analysis (327).

For example, when a cardholder has only used a credit card during aportion of the time period under analysis, the transaction records (301)during the time period may not reflect the consistent behavior of thecardholder for the entire time period. Consistency can be checked invarious ways. In one example, if the total number of transactions duringthe first and last months of the time period under analysis is zero, thetransactions associated with the entity ID (322) are inconsistent in thetime period and thus are not used in the cluster analysis (329) and/orthe factor analysis (327). Other criteria can be formulated to detectinconsistency in the transactions.

In FIG. 3, the computation models (e.g., as represented by the variabledefinitions (309)) are applied (359) to the remaining account data(e.g., transaction records (301)) to obtain data samples for thevariables. The data points associated with the entities, other thanthose whose transactions fail to meet the minimum requirements foractivity, consistency, diversity, etc., are used in factor analysis(327) and cluster analysis (329).

In FIG. 3, the data samples (e.g., variable values (321)) are used toperform (361) factor analysis (327) to identify factor solutions (e.g.,factor definitions (331)). The factor solutions can be adjusted (363) toimprove similarity in factor values of different sets of transactiondata (109). For example, factor definitions (331) can be applied to thetransactions in the time period under analysis (e.g., the past twelvemonths) and be applied separately to the transactions in a prior timeperiod (e.g., the twelve months before the past twelve months) to obtaintwo sets of factor values. The factor definitions (331) can be adjustedto improve the correlation between the two set of factor values.

The data samples can also be used to perform (365) cluster analysis(329) to identify cluster solutions (e.g., cluster definitions (333)).The cluster solutions can be adjusted (367) to improve similarity incluster identifications based on different sets of transaction data(109). For example, cluster definitions (333) can be applied to thetransactions in the time period under analysis (e.g., the past twelvemonths) and be applied separately to the transactions in a prior timeperiod (e.g., the twelve months before the past twelve months) to obtaintwo sets of cluster identifications for various entities. The clusterdefinitions (333) can be adjusted to improve the correlation between thetwo set of cluster identifications.

In one embodiment, the number of clusters is determined from clusteringanalysis. For example, a set of cluster seeds can be initiallyidentified and used to run a known clustering algorithm. The sizes ofdata points in the clusters are then examined. When a cluster containsless than a predetermined number of data points, the cluster may beeliminated to rerun the clustering analysis.

In one embodiment, standardizing entropy is added to the clustersolution to obtain improved results.

In one embodiment, human understandable characteristics of the factorsand clusters are identified (369) to name the factors and clusters. Forexample, when the spending behavior of a cluster appears to be thebehavior of an internet loyalist, the cluster can be named “internetloyalist” such that if a cardholder is found to be in the “internetloyalist” cluster, the spending preferences and patterns of thecardholder can be easily perceived.

In one embodiment, the factor analysis (327) and the cluster analysis(329) are performed periodically (e.g., once a year, or six months) toupdate the factor definitions (331) and the cluster definitions (333),which may change as the economy and the society change over time.

In FIG. 3, transaction data (109) are summarized (371) using the factorsolutions and cluster solutions to generate the aggregated spendingprofile (341). The aggregated spending profile (341) can be updated morefrequently than the factor solutions and cluster solutions, when the newtransaction data (109) becomes available. For example, the aggregatedspending profile (341) may be updated quarterly or monthly.

Various tweaks and adjustments can be made for the variables (e.g., 313,315) used for the factor analysis (327) and the cluster analysis (329).For example, the transaction records (301) may be filtered, weighted orconstrained, according to different rules to improve the capabilities ofthe aggregated measurements in indicating certain aspects of thespending behavior of the customers.

For example, in one embodiment, the variables (e.g., 313, 315) arenormalized and/or standardized (e.g., using statistical average, mean,and/or variance).

For example, the variables (e.g., 313, 315) for the aggregatedmeasurements can be tuned, via filtering and weighting, to predict thefuture trend of spending behavior (e.g., for advertisement selection),to identify abnormal behavior (e.g., for fraud prevention), or toidentify a change in spending pattern (e.g., for advertisement audiencemeasurement), etc. The aggregated measurements, the factor values (344),and/or the cluster ID (343) generated from the aggregated measurementscan be used in a transaction profile (127 or 341) to define the behaviorof an account, an individual, a family, etc.

In one embodiment, the transaction data (109) are aged to provide moreweight to recent data than older data. In other embodiments, thetransaction data (109) are reverse aged. In further embodiments, thetransaction data (109) are seasonally adjusted.

In one embodiment, the variables (e.g., 313, 315) are constrained toeliminate extreme outliers. For example, the minimum values and themaximum values of the spending amounts (315) may be constrained based onvalues at certain percentiles (e.g., the value at one percentile as theminimum and the value at 99 percentile as the maximum) and/or certainpredetermined values. In one embodiment, the spending frequencyvariables (313) are constrained based on values at certain percentilesand median values. For example, the minimum value for a spendingfrequency variable (313) may be constrained at P₁−k×(M−P₁), where P₁ isthe one percentile value, M the median value, and k a predeterminedconstant (e.g., 0.1). For example, the maximum value for a spendingfrequency variable (313) may be constrained at P₉₉+a×(P₉₉−M), where P₉₉is the 99 percentile value, M the median value, and k a predeterminedconstant (e.g., 0.1).

In one embodiment, variable pruning is performed to reduce the number ofvariables (e.g., 313, 315) that have less impact on cluster solutionsand/or factor solutions. For example, variables with standard variationless than a predetermined threshold (e.g., 0.1) may be discarded for thepurpose of cluster analysis (329). For example, analysis of variance(ANOVA) can be performed to identify and remove variables that are nomore significant than a predetermined threshold.

The aggregated spending profile (341) can provide information onspending behavior for various application areas, such as marketing,fraud detection and prevention, creditworthiness assessment, loyaltyanalytics, targeting of offers, etc.

For example, clusters can be used to optimize offers for various groupswithin an advertisement campaign. The use of factors and clusters totarget advertisement can improve the speed of producing targetingmodels. For example, using variables based on factors and clusters (andthus eliminating the need to use a large number of convention variables)can improve predictive models and increase efficiency of targeting byreducing the number of variables examined. The variables formulatedbased on factors and/or clusters can be used with other variables tobuild predictive models based on spending behaviors.

In one embodiment, the aggregated spending profile (341) can be used tomonitor risks in transactions. Factor values are typically consistentover time for each entity. An abrupt change in some of the factor valuesmay indicate a change in financial conditions, or a fraudulent use ofthe account. Models formulated using factors and clusters can be used toidentify a series of transactions that do not follow a normal patternspecified by the factor values (344) and/or the cluster ID (343).Potential bankruptcies can be predicted by analyzing the change offactor values over time; and significant changes in spending behaviormay be detected to stop and/or prevent fraudulent activities.

For example, the factor values (344) can be used in regression modelsand/or neural network models for the detection of certain behaviors orpatterns. Since factors are relatively non-collinear, the factors canwork well as independent variables. For example, factors and clusterscan be used as independent variables in tree models.

For example, surrogate accounts can be selected for the construction ofa quasi-control group. For example, for a given account A that is in onecluster, the account B that is closest to the account A in the samecluster can be selected as a surrogate account of the account B. Thecloseness can be determined by certain values in the aggregated spendingprofile (341), such as factor values (344), category distribution (346),etc. For example, a Euclidian distance defined based on the set ofvalues from the aggregated spending profile (341) can be used to comparethe distances between the accounts. Once identified, the surrogateaccount can be used to reduce or eliminate bias in measurements. Forexample, to determine the effect of an advertisement, the spendingpattern response of the account A that is exposed to the advertisementcan be compared to the spending pattern response of the account B thatis not exposed to the advertisement.

For example, the aggregated spending profile (341) can be used insegmentation and/or filtering analysis, such as selecting cardholdershaving similar spending behaviors identified via factors and/or clustersfor targeted advertisement campaigns, and selecting and determining agroup of merchants that could be potentially marketed towardscardholders originating in a given cluster (e.g., for bundled offers).For example, a query interface can be provided to allow the query toidentify a targeted population based on a set of criteria formulatedusing the values of clusters and factors.

For example, the aggregated spending profile (341) can be used in aspending comparison report, such as comparing a sub-population ofinterest against the overall population, determining how clusterdistributions and mean factor values differ, and building reports formerchants and/or issuers for benchmarking purposes. For example, reportscan be generated according to clusters in an automated way for themerchants. For example, the aggregated spending profile (341) can beused in geographic reports by identifying geographic areas wherecardholders shop most frequently and comparing predominant spendinglocations with cardholder residence locations.

In one embodiment, the profile generator (121) provides affinityrelationship data in the transaction profiles (127) so that thetransaction profiles (127) can be shared with business partners withoutcompromising the privacy of the users (101) and the transaction details.

For example, in one embodiment, the profile generator (121) is toidentify clusters of entities (e.g., accounts, cardholders, families,businesses, cities, regions, etc.) based on the spending patterns of theentities. The clusters represent entity segments identified based on thespending patterns of the entities reflected in the transaction data(109) or the transaction records (301).

In one embodiment, the clusters correspond to cells or regions in themathematical space that contain the respective groups of entities. Forexample, the mathematical space representing the characteristics ofusers (101) may be divided into clusters (cells or regions). Forexample, the cluster analysis (329) may identify one cluster in the cellor region that contains a cluster of entity IDs (e.g., 322) in the spacehaving a plurality of dimensions corresponding to the variables (e.g.,313 and 315). For example, a cluster can also be identified as a cell orregion in a space defined by the factors using the factor definitions(331) generated from the factor analysis (327).

In one embodiment, the parameters used in the aggregated spendingprofile (341) can be used to define a segment or a cluster of entities.For example, a value for the cluster ID (343) and a set of ranges forthe factor values (344) and/or other values can be used to define asegment.

In one embodiment, a set of clusters are standardized to represent thepredilection of entities in various groups for certain products orservices. For example, a set of standardized clusters can be formulatedfor people who have shopped, for example, at home improvement stores.The cardholders in the same cluster have similar spending behavior.

In one embodiment, the tendency or likelihood of a user (101) being in aparticular cluster (i.e. the user's affinity to the cell) can becharacterized using a value, based on past purchases. The same user(101) may have different affinity values for different clusters.

For example, a set of affinity values can be computed for an entity,based on the transaction records (301), to indicate the closeness orpredilection of the entity to the set of standardized clusters. Forexample, a cardholder who has a first value representing affinity of thecardholder to a first cluster may have a second value representingaffinity of the cardholder to a second cluster. For example, if aconsumer buys a lot of electronics, the affinity value of the consumerto the electronics cluster is high.

In one embodiment, other indicators are formulated across the merchantcommunity and cardholder behavior and provided in the profile (e.g., 127or 341) to indicate the risk of a transaction.

In one embodiment, the relationship of a pair of values from twodifferent clusters provides an indication of the likelihood that theuser (101) is in one of the two cells, if the user (101) is shown to bein the other cell. For example, if the likelihood of the user (101) topurchase each of two types of products is known, the scores can be usedto determine the likelihood of the user (101) buying one of the twotypes of products if the user (101) is known to be interested in theother type of products. In one embodiment, a map of the values for theclusters is used in a profile (e.g., 127 or 341) to characterize thespending behavior of the user (101) (or other types of entities, such asa family, company, neighborhood, city, or other types of groups definedby other aggregate parameters, such as time of day, etc.).

In one embodiment, the clusters and affinity information arestandardized to allow sharing between business partners, such astransaction processing organizations, search providers, and marketers.Purchase statistics and search statistics are generally described indifferent ways. For example, purchase statistics are based on merchants,merchant categories, SKU numbers, product descriptions, etc.; and searchstatistics are based on search terms. Once the clusters arestandardized, the clusters can be used to link purchase informationbased merchant categories (and/or SKU numbers, product descriptions)with search information based on search terms. Thus, search predilectionand purchase predilection can be mapped to each other.

In one embodiment, the purchase data and the search data (or other thirdparty data) are correlated based on mapping to the standardized clusters(cells or segments). The purchase data and the search data (or otherthird party data) can be used together to provide benefits or offers(e.g., coupons) to consumers. For example, standardized clusters can beused as a marketing tool to provide relevant benefits, includingcoupons, statement credits, or the like to consumers who are within orare associated with common clusters. For example, a data exchangeapparatus may obtain cluster data based on consumer search engine dataand actual payment transaction data to identify like groups ofindividuals who may respond favorably to particular types of benefits,such as coupons and statement credits.

Details about aggregated spending profile (341) in one embodiment areprovided in U.S. patent application Ser. No. 12/777,173, filed May 10,2010, assigned Pub. No. 2010/0306032, and entitled “Systems and Methodsto Summarize Transaction Data,” the disclosure of which is herebyincorporated herein by reference.

Transaction Data Based Portal

In FIG. 1, the transaction terminal (105) initiates the transaction fora user (101) (e.g., a customer) for processing by a transaction handler(103). The transaction handler (103) processes the transaction andstores transaction data (109) about the transaction, in connection withaccount data (111), such as the account profile of an account of theuser (101). The account data (111) may further include data about theuser (101), collected from issuers or merchants, and/or other sources,such as social networks, credit bureaus, merchant provided information,address information, etc. In one embodiment, a transaction may beinitiated by a server (e.g., based on a stored schedule for recurrentpayments).

Over a period of time, the transaction handler (103) accumulates thetransaction data (109) from transactions initiated at differenttransaction terminals (e.g., 105) for different users (e.g., 101). Thetransaction data (109) thus includes information on purchases made byvarious users (e.g., 101) at various times via different purchasesoptions (e.g., online purchase, offline purchase from a retail store,mail order, order via phone, etc.)

In one embodiment, the accumulated transaction data (109) and thecorresponding account data (111) are used to generate intelligenceinformation about the purchase behavior, pattern, preference, tendency,frequency, trend, amount and/or propensity of the users (e.g., 101), asindividuals or as a member of a group. The intelligence information canthen be used to generate, identify and/or select targeted advertisementsfor presentation to the user (101) on the point of interaction (107),during a transaction, after a transaction, or when other opportunitiesarise.

FIG. 4 shows a system to provide information based on transaction data(109) according to one embodiment. In FIG. 4, the transaction handler(103) is coupled between an issuer processor (145) and an acquirerprocessor (147) to facilitate authorization and settlement oftransactions between a consumer account (146) and a merchant account(148). The transaction handler (103) records the transactions in thedata warehouse (149). The portal (143) is coupled to the data warehouse(149) to provide information based on the transaction records (301),such as the transaction profiles (127) or aggregated spending profile(341). The portal (143) may be implemented as a web portal, a telephonegateway, a file/data server, etc.

In one embodiment, the portal (143) is configured to receive queriesidentifying search criteria from the profile selector (129), theadvertisement selector (133) and/or third parties and in response, toprovide transaction-based intelligence requested by the queries.

For example, in one embodiment, a query is to specify a plurality ofaccount holders to request the portal (143) to deliver the transactionprofiles (127) of account holders in a batch mode.

For example, in one embodiment, a query is to identify the user (101) torequest the user specific profile (131), or the aggregated spendingprofile (341), of the user (101). The user (101) may be identified usingthe account data (111), such as the account number (302), or the userdata (125) such as browser cookie ID, IP address, etc.

For example, in one embodiment, a query is to identify a retaillocation; and the portal (143) is to provide a profile (e.g., 341) thatsummarizes the aggregated spending patterns of users who have shopped atthe retail location within a period of time.

For example, in one embodiment, a query is to identify a geographicallocation; and the portal (143) is to provide a profile (e.g., 341) thatsummarizes the aggregated spending patterns of users who have been to,or who are expected to visit, the geographical location within a periodof time (e.g., as determined or predicted based on the locations of thepoint of interactions (e.g., 107) of the users).

For example, in one embodiment, a query is to identify a geographicalarea; and the portal (143) is to provide a profile (e.g., 341) thatsummarizes the aggregated spending patterns of users who reside in thegeographical area (e.g., as determined by the account data (111), or whohave made transactions within the geographical area with a period oftime (e.g., as determined by the locations of the transaction terminals(e.g., 105) used to process the transactions).

In one embodiment, the portal (143) is configured to register certainusers (101) for various programs, such as a loyalty program to providerewards and/or offers to the users (101).

In one embodiment, the portal (143) is to register the interest of users(101), or to obtain permissions from the users (101) to gather furtherinformation about the users (101), such as data capturing purchasedetails, online activities, etc.

In one embodiment, the user (101) may register via the issuer; and theregistration data in the consumer account (146) may propagate to thedata warehouse (149) upon approval from the user (101).

In one embodiment, the portal (143) is to register merchants and provideservices and/or information to merchants.

In one embodiment, the portal (143) is to receive information from thirdparties, such as search engines, merchants, websites, etc. The thirdparty data can be correlated with the transaction data (109) to identifythe relationships between purchases and other events, such as searches,news announcements, conferences, meetings, etc., and improve theprediction capability and accuracy.

In FIG. 4, the consumer account (146) is under the control of the issuerprocessor (145). The consumer account (146) may be owned by anindividual, or an organization such as a business, a school, etc. Theconsumer account (146) may be a credit account, a debit account, or astored value account. The issuer may provide the consumer (e.g., user(101)) an account identification device (141) to identify the consumeraccount (146) using the account information (142). The respectiveconsumer of the account (146) can be called an account holder or acardholder, even when the consumer is not physically issued a card, orthe account identification device (141), in one embodiment. The issuerprocessor (145) is to charge the consumer account (146) to pay forpurchases.

In one embodiment, the account identification device (141) is a plasticcard having a magnetic strip storing account information (142)identifying the consumer account (146) and/or the issuer processor(145). Alternatively, the account identification device (141) is asmartcard having an integrated circuit chip storing at least the accountinformation (142). In one embodiment, the account identification device(141) includes a mobile phone having an integrated smartcard.

In one embodiment, the account information (142) is printed or embossedon the account identification device (141). The account information(142) may be printed as a bar code to allow the transaction terminal(105) to read the information via an optical scanner. The accountinformation (142) may be stored in a memory of the accountidentification device (141) and configured to be read via wireless,contactless communications, such as near field communications viamagnetic field coupling, infrared communications, or radio frequencycommunications. Alternatively, the transaction terminal (105) mayrequire contact with the account identification device (141) to read theaccount information (142) (e.g., by reading the magnetic strip of a cardwith a magnetic strip reader).

In one embodiment, the transaction terminal (105) is configured totransmit an authorization request message to the acquirer processor(147). The authorization request includes the account information (142),an amount of payment, and information about the merchant (e.g., anindication of the merchant account (148)). The acquirer processor (147)requests the transaction handler (103) to process the authorizationrequest, based on the account information (142) received in thetransaction terminal (105). The transaction handler (103) routes theauthorization request to the issuer processor (145) and may process andrespond to the authorization request when the issuer processor (145) isnot available. The issuer processor (145) determines whether toauthorize the transaction based at least in part on a balance of theconsumer account (146).

In one embodiment, the transaction handler (103), the issuer processor(145), and the acquirer processor (147) may each include a subsystem toidentify the risk in the transaction and may reject the transactionbased on the risk assessment.

In one embodiment, the account identification device (141) includessecurity features to prevent unauthorized uses of the consumer account(146), such as a logo to show the authenticity of the accountidentification device (141), encryption to protect the accountinformation (142), etc.

In one embodiment, the transaction terminal (105) is configured tointeract with the account identification device (141) to obtain theaccount information (142) that identifies the consumer account (146)and/or the issuer processor (145). The transaction terminal (105)communicates with the acquirer processor (147) that controls themerchant account (148) of a merchant. The transaction terminal (105) maycommunicate with the acquirer processor (147) via a data communicationconnection, such as a telephone connection, an Internet connection, etc.The acquirer processor (147) is to collect payments into the merchantaccount (148) on behalf of the merchant.

In one embodiment, the transaction terminal (105) is a POS terminal at atraditional, offline, “brick and mortar” retail store. In anotherembodiment, the transaction terminal (105) is an online server thatreceives account information (142) of the consumer account (146) fromthe user (101) through a web connection. In one embodiment, the user(101) may provide account information (142) through a telephone call,via verbal communications with a representative of the merchant; and therepresentative enters the account information (142) into the transactionterminal (105) to initiate the transaction.

In one embodiment, the account information (142) can be entered directlyinto the transaction terminal (105) to make payment from the consumeraccount (146), without having to physically present the accountidentification device (141). When a transaction is initiated withoutphysically presenting an account identification device (141), thetransaction is classified as a “card-not-present” (CNP) transaction.

In one embodiment, the issuer processor (145) may control more than oneconsumer account (146); the acquirer processor (147) may control morethan one merchant account (148); and the transaction handler (103) isconnected between a plurality of issuer processors (e.g., 145) and aplurality of acquirer processors (e.g., 147). An entity (e.g., bank) mayoperate both an issuer processor (145) and an acquirer processor (147).

In one embodiment, the transaction handler (103), the issuer processor(145), the acquirer processor (147), the transaction terminal (105), theportal (143), and other devices and/or services accessing the portal(143) are connected via communications networks, such as local areanetworks, cellular telecommunications networks, wireless wide areanetworks, wireless local area networks, an intranet, and Internet. Inone embodiment, dedicated communication channels are used between thetransaction handler (103) and the issuer processor (145), between thetransaction handler (103) and the acquirer processor (147), and/orbetween the portal (143) and the transaction handler (103).

In one embodiment, the transaction handler (103) uses the data warehouse(149) to store the records about the transactions, such as thetransaction records (301) or transaction data (109). In one embodiment,the transaction handler (103) includes a powerful computer, or clusterof computers functioning as a unit, controlled by instructions stored ona computer readable medium.

In one embodiment, the transaction handler (103) is configured tosupport and deliver authorization services, exception file services, andclearing and settlement services. In one embodiment, the transactionhandler (103) has a subsystem to process authorization requests andanother subsystem to perform clearing and settlement services.

In one embodiment, the transaction handler (103) is configured toprocess different types of transactions, such credit card transactions,debit card transactions, prepaid card transactions, and other types ofcommercial transactions.

In one embodiment, the transaction handler (103) facilitates thecommunications between the issuer processor (145) and the acquirerprocessor (147).

In one embodiment, the transaction handler (103) is coupled to theportal (143) (and/or the profile selector (129), the advertisementselector (133), the media controller (115)) to charge the fees for theservices of providing the transaction-based intelligence informationand/or advertisement.

For example, in one embodiment, the system illustrated in FIG. 1 isconfigured to deliver advertisements to the point of interaction (107)of the user (101), based on the transaction-based intelligenceinformation; and the transaction handler (103) is configured to chargethe advertisement fees to the account of the advertiser in communicationwith the issuer processor in control of the account of the advertiser.The advertisement fees may be charged in response to the presentation ofthe advertisement, or in response to the completion of a pre-determinednumber of presentations, or in response to a transaction resulted fromthe presentation of the advertisement. In one embodiment, thetransaction handler (103) is configured to a periodic fee (e.g., monthlyfee, annual fee) to the account of the advertiser in communication withthe respective issuer processor that is similar to the issuer processor(145) of the consumer account (146).

For example, in one embodiment, the portal (143) is configured toprovide transaction-based intelligence information in response to thequeries received in the portal (143). The portal (143) is to identifythe requesters (e.g., via an authentication, or the address of therequesters) and instruct the transaction handler (103) to charge theconsumer accounts (e.g., 146) of the respective requesters for thetransaction-based intelligence information. In one embodiment, theaccounts of the requesters are charged in response to the delivery ofthe intelligence information via the portal (143). In one embodiment,the accounts of the requesters are charged a periodic subscription feefor the access to the query capability of the portal (143).

In one embodiment, the information service provided by the systemillustrated in FIG. 1 includes multiple parties, such as one entityoperating the transaction handler (103), one entity operating theadvertisement data (135), one entity operating the user tracker (113),one entity operating the media controller (115), etc. The transactionhandler (103) is used to generate transactions to settle the fees,charges and/or divide revenues using the accounts of the respectiveparties. In one embodiment, the account information of the parties isstored in the data warehouse (149) coupled to the transaction handler(103). In some embodiments, a separate billing engine is used togenerate the transactions to settle the fees, charges and/or dividerevenues.

In one embodiment, the transaction terminal (105) is configured tosubmit the authorized transactions to the acquirer processor (147) forsettlement. The amount for the settlement may be different from theamount specified in the authorization request. The transaction handler(103) is coupled between the issuer processor (145) and the acquirerprocessor (147) to facilitate the clearing and settling of thetransaction. Clearing includes the exchange of financial informationbetween the issuer processor (145) and the acquirer processor (147); andsettlement includes the exchange of funds.

In one embodiment, the issuer processor (145) is to provide funds tomake payments on behalf of the consumer account (146). The acquirerprocessor (147) is to receive the funds on behalf of the merchantaccount (148). The issuer processor (145) and the acquirer processor(147) communicate with the transaction handler (103) to coordinate thetransfer of funds for the transaction. In one embodiment, the funds aretransferred electronically.

In one embodiment, the transaction terminal (105) may submit atransaction directly for settlement, without having to separately submitan authorization request.

In one embodiment, the portal (143) provides a user interface to allowthe user (101) to organize the transactions in one or more consumeraccounts (146) of the user with one or more issuers. The user (101) mayorganize the transactions using information and/or categories identifiedin the transaction records (301), such as merchant category (306),transaction date (303), amount (304), etc. Examples and techniques inone embodiment are provided in U.S. patent application Ser. No.11/378,215, filed Mar. 16, 2006, assigned Pub. No. 2007/0055597, andentitled “Method and System for Manipulating Purchase Information,” thedisclosure of which is hereby incorporated herein by reference.

In one embodiment, the portal (143) provides transaction basedstatistics, such as indicators for retail spending monitoring,indicators for merchant benchmarking, industry/market segmentation,indicators of spending patterns, etc. Further examples can be found inU.S. Pat. application Ser. No. 12/191,796, filed Aug. 14, 2008, assignedPub. No. 2009/0048884, and entitled “Merchant Benchmarking Tool,” U.S.patent application Ser. No. 12/940,562, filed Nov. 5, 2010, and U.S.patent application Ser. No. 12/940,664, filed Nov. 5, 2010, thedisclosures of which applications are hereby incorporated herein byreference.

Transaction Terminal

FIG. 5 illustrates a transaction terminal according to one embodiment.In FIG. 5, the transaction terminal (105) is configured to interact withan account identification device (141) to obtain account information(142) about the consumer account (146).

In one embodiment, the transaction terminal (105) includes a memory(167) coupled to the processor (151), which controls the operations of areader (163), an input device (153), an output device (165) and anetwork interface (161). The memory (167) may store instructions for theprocessor (151) and/or data, such as an identification that isassociated with the merchant account (148).

In one embodiment, the reader (163) includes a magnetic strip reader. Inanother embodiment, the reader (163) includes a contactless reader, suchas a radio frequency identification (RFID) reader, a near fieldcommunications (NFC) device configured to read data via magnetic fieldcoupling (in accordance with ISO standard 14443/NFC), a Bluetoothtransceiver, a WiFi transceiver, an infrared transceiver, a laserscanner, etc.

In one embodiment, the input device (153) includes key buttons that canbe used to enter the account information (142) directly into thetransaction terminal (105) without the physical presence of the accountidentification device (141). The input device (153) can be configured toprovide further information to initiate a transaction, such as apersonal identification number (PIN), password, zip code, etc. that maybe used to access the account identification device (141), or incombination with the account information (142) obtained from the accountidentification device (141).

In one embodiment, the output device (165) may include a display, aspeaker, and/or a printer to present information, such as the result ofan authorization request, a receipt for the transaction, anadvertisement, etc.

In one embodiment, the network interface (161) is configured tocommunicate with the acquirer processor (147) via a telephoneconnection, an Internet connection, or a dedicated data communicationchannel.

In one embodiment, the instructions stored in the memory (167) areconfigured at least to cause the transaction terminal (105) to send anauthorization request message to the acquirer processor (147) toinitiate a transaction. The transaction terminal (105) may or may notsend a separate request for the clearing and settling of thetransaction. The instructions stored in the memory (167) are alsoconfigured to cause the transaction terminal (105) to perform othertypes of functions discussed in this description.

In one embodiment, a transaction terminal (105) may have fewercomponents than those illustrated in FIG. 5. For example, in oneembodiment, the transaction terminal (105) is configured for“card-not-present” transactions; and the transaction terminal (105) doesnot have a reader (163).

In one embodiment, a transaction terminal (105) may have more componentsthan those illustrated in FIG. 5. For example, in one embodiment, thetransaction terminal (105) is an ATM machine, which includes componentsto dispense cash under certain conditions.

Account Identification Device

FIG. 6 illustrates an account identifying device according to oneembodiment. In FIG. 6, the account identification device (141) isconfigured to carry account information (142) that identifies theconsumer account (146).

In one embodiment, the account identification device (141) includes amemory (167) coupled to the processor (151), which controls theoperations of a communication device (159), an input device (153), anaudio device (157) and a display device (155). The memory (167) maystore instructions for the processor (151) and/or data, such as theaccount information (142) associated with the consumer account (146).

In one embodiment, the account information (142) includes an identifieridentifying the issuer (and thus the issuer processor (145)) among aplurality of issuers, and an identifier identifying the consumer accountamong a plurality of consumer accounts controlled by the issuerprocessor (145). The account information (142) may include an expirationdate of the account identification device (141), the name of theconsumer holding the consumer account (146), and/or an identifieridentifying the account identification device (141) among a plurality ofaccount identification devices associated with the consumer account(146).

In one embodiment, the account information (142) may further include aloyalty program account number, accumulated rewards of the consumer inthe loyalty program, an address of the consumer, a balance of theconsumer account (146), transit information (e.g., a subway or trainpass), access information (e.g., access badges), and/or consumerinformation (e.g., name, date of birth), etc.

In one embodiment, the memory includes a nonvolatile memory, such asmagnetic strip, a memory chip, a flash memory, a Read Only Memory (ROM),etc. to store the account information (142).

In one embodiment, the information stored in the memory (167) of theaccount identification device (141) may also be in the form of datatracks that are traditionally associated with credits cards. Such tracksinclude Track 1 and Track 2. Track 1 (“International Air TransportAssociation”) stores more information than Track 2, and contains thecardholder's name as well as the account number and other discretionarydata. Track 1 is sometimes used by airlines when securing reservationswith a credit card. Track 2 (“American Banking Association”) iscurrently most commonly used and is read by ATMs and credit cardcheckers. The ABA (American Banking Association) designed thespecifications of Track 1 and banks abide by it. It contains thecardholder's account number, encrypted PIN, and other discretionarydata.

In one embodiment, the communication device (159) includes asemiconductor chip to implement a transceiver for communication with thereader (163) and an antenna to provide and/or receive wireless signals.

In one embodiment, the communication device (159) is configured tocommunicate with the reader (163). The communication device (159) mayinclude a transmitter to transmit the account information (142) viawireless transmissions, such as radio frequency signals, magneticcoupling, or infrared, Bluetooth or WiFi signals, etc.

In one embodiment, the account identification device (141) is in theform of a mobile phone, personal digital assistant (PDA), etc. The inputdevice (153) can be used to provide input to the processor (151) tocontrol the operation of the account identification device (141); andthe audio device (157) and the display device (155) may present statusinformation and/or other information, such as advertisements or offers.The account identification device (141) may include further componentsthat are not shown in FIG. 6, such as a cellular communicationssubsystem.

In one embodiment, the communication device (159) may access the accountinformation (142) stored on the memory (167) without going through theprocessor (151).

In one embodiment, the account identification device (141) has fewercomponents than those illustrated in FIG. 6. For example, an accountidentification device (141) does not have the input device (153), theaudio device (157) and the display device (155) in one embodiment; andin another embodiment, an account identification device (141) does nothave components (151-159).

For example, in one embodiment, an account identification device (141)is in the form of a debit card, a credit card, a smartcard, or aconsumer device that has optional features such as magnetic strips, orsmartcards.

An example of an account identification device (141) is a magnetic stripattached to a plastic substrate in the form of a card. The magneticstrip is used as the memory (167) of the account identification device(141) to provide the account information (142). Consumer information,such as account number, expiration date, and consumer name may beprinted or embossed on the card. A semiconductor chip implementing thememory (167) and the communication device (159) may also be embedded inthe plastic card to provide account information (142) in one embodiment.In one embodiment, the account identification device (141) has thesemiconductor chip but not the magnetic strip.

In one embodiment, the account identification device (141) is integratedwith a security device, such as an access card, a radio frequencyidentification (RFID) tag, a security card, a transponder, etc.

In one embodiment, the account identification device (141) is a handheldand compact device. In one embodiment, the account identification device(141) has a size suitable to be placed in a wallet or pocket of theconsumer.

Some examples of an account identification device (141) include a creditcard, a debit card, a stored value device, a payment card, a gift card,a smartcard, a smart media card, a payroll card, a health care card, awrist band, a keychain device, a supermarket discount card, atransponder, and a machine readable medium containing accountinformation (142).

Point of Interaction

In one embodiment, the point of interaction (107) is to provide anadvertisement to the user (101), or to provide information derived fromthe transaction data (109) to the user (101).

In one embodiment, an advertisement is a marketing interaction which mayinclude an announcement and/or an offer of a benefit, such as adiscount, incentive, reward, coupon, gift, cash back, or opportunity(e.g., special ticket/admission). An advertisement may include an offerof a product or service, an announcement of a product or service, or apresentation of a brand of products or services, or a notice of events,facts, opinions, etc. The advertisements can be presented in text,graphics, audio, video, or animation, and as printed matter, webcontent, interactive media, etc. An advertisement may be presented inresponse to the presence of a financial transaction card, or in responseto a financial transaction card being used to make a financialtransaction, or in response to other user activities, such as browsing aweb page, submitting a search request, communicating online, entering awireless communication zone, etc. In one embodiment, the presentation ofadvertisements may be not a result of a user action.

In one embodiment, the point of interaction (107) can be one of variousendpoints of the transaction network, such as point of sale (POS)terminals, automated teller machines (ATMs), electronic kiosks (orcomputer kiosks or interactive kiosks), self-assist checkout terminals,vending machines, gas pumps, websites of banks (e.g., issuer banks oracquirer banks of credit cards), bank statements (e.g., credit cardstatements), websites of the transaction handler (103), websites ofmerchants, checkout websites or web pages for online purchases, etc.

In one embodiment, the point of interaction (107) may be the same as thetransaction terminal (105), such as a point of sale (POS) terminal, anautomated teller machine (ATM), a mobile phone, a computer of the userfor an online transaction, etc. In one embodiment, the point ofinteraction (107) may be co-located with, or near, the transactionterminal (105) (e.g., a video monitor or display, a digital sign), orproduced by the transaction terminal (e.g., a receipt produced by thetransaction terminal (105)). In one embodiment, the point of interaction(107) may be separate from and not co-located with the transactionterminal (105), such as a mobile phone, a personal digital assistant, apersonal computer of the user, a voice mail box of the user, an emailinbox of the user, a digital sign, etc.

For example, the advertisements can be presented on a portion of mediafor a transaction with the customer, which portion might otherwise beunused and thus referred to as a “white space” herein. A white space canbe on a printed matter (e.g., a receipt printed for the transaction, ora printed credit card statement), on a video display (e.g., a displaymonitor of a POS terminal for a retail transaction, an ATM for cashwithdrawal or money transfer, a personal computer of the customer foronline purchases), or on an audio channel (e.g., an interactive voiceresponse (IVR) system for a transaction over a telephonic device).

In one embodiment, the white space is part of a media channel availableto present a message from the transaction handler (103) in connectionwith the processing of a transaction of the user (101). In oneembodiment, the white space is in a media channel that is used to reportinformation about a transaction of the user (101), such as anauthorization status, a confirmation message, a verification message, auser interface to verify a password for the online use of the accountinformation (142), a monthly statement, an alert or a report, or a webpage provided by the portal (143) to access a loyalty program associatedwith the consumer account (146) or a registration program.

In other embodiments, the advertisements can also be presented via othermedia channels which may not involve a transaction processed by thetransaction handler (103). For example, the advertisements can bepresented on publications or announcements (e.g., newspapers, magazines,books, directories, radio broadcasts, television, digital signage, etc.,which may be in an electronic form, or in a printed or painted form).The advertisements may be presented on paper, on websites, onbillboards, on digital signs, or on audio portals.

In one embodiment, the transaction handler (103) purchases the rights touse the media channels from the owner or operators of the media channelsand uses the media channels as advertisement spaces. For example, whitespaces at a point of interaction (e.g., 107) with customers fortransactions processed by the transaction handler (103) can be used todeliver advertisements relevant to the customers conducting thetransactions; and the advertisement can be selected based at least inpart on the intelligence information derived from the accumulatedtransaction data (109) and/or the context at the point of interaction(107) and/or the transaction terminal (105).

In general, a point of interaction (e.g., 107) may or may not be capableof receiving inputs from the customers, and may or may not co-locatedwith a transaction terminal (e.g., 105) that initiates the transactions.The white spaces for presenting the advertisement on the point ofinteraction (107) may be on a portion of a geographical display space(e.g., on a screen), or on a temporal space (e.g., in an audio stream).

In one embodiment, the point of interaction (107) may be used toprimarily to access services not provided by the transaction handler(103), such as services provided by a search engine, a social networkingwebsite, an online marketplace, a blog, a news site, a televisionprogram provider, a radio station, a satellite, a publisher, etc.

In one embodiment, a consumer device is used as the point of interaction(107), which may be a non-portable consumer device or a portablecomputing device. The consumer device is to provide media content to theuser (101) and may receive input from the user (101).

Examples of non-portable consumer devices include a computer terminal, atelevision set, a personal computer, a set-top box, or the like.Examples of portable consumer devices include a portable computer, acellular phone, a personal digital assistant (PDA), a pager, a securitycard, a wireless terminal, or the like. The consumer device may beimplemented as a data processing system as illustrated in FIG. 7, withmore or fewer components.

In one embodiment, the consumer device includes an accountidentification device (141). For example, a smart card used as anaccount identification device (141) is integrated with a mobile phone,or a personal digital assistant (PDA).

In one embodiment, the point of interaction (107) is integrated with atransaction terminal (105). For example, a self-service checkoutterminal includes a touch pad to interact with the user (101); and anATM machine includes a user interface subsystem to interact with theuser (101).

Hardware

In one embodiment, a computing apparatus is configured to include someof the modules or components illustrated in FIGS. 1 and 4, such as thetransaction handler (103), the profile generator (121), the mediacontroller (115), the portal (143), the profile selector (129), theadvertisement selector (133), the user tracker (113), the correlator,and their associated storage devices, such as the data warehouse (149).

In one embodiment, at least some of the modules or componentsillustrated in FIGS. 1 and 4, such as the transaction handler (103), thetransaction terminal (105), the point of interaction (107), the usertracker (113), the media controller (115), the correlator (117), theprofile generator (121), the profile selector (129), the advertisementselector (133), the portal (143), the issuer processor (145), theacquirer processor (147), and the account identification device (141),can be implemented as a computer system, such as a data processingsystem illustrated in FIG. 7, with more or fewer components. Some of themodules may share hardware or be combined on a computer system. In oneembodiment, a network of computers can be used to implement one or moreof the modules.

Further, the data illustrated in FIG. 1, such as transaction data (109),account data (111), transaction profiles (127), and advertisement data(135), can be stored in storage devices of one or more computersaccessible to the corresponding modules illustrated in FIG. 1. Forexample, the transaction data (109) can be stored in the data warehouse(149) that can be implemented as a data processing system illustrated inFIG. 7, with more or fewer components.

In one embodiment, the transaction handler (103) is a payment processingsystem, or a payment card processor, such as a card processor for creditcards, debit cards, etc.

FIG. 7 illustrates a data processing system according to one embodiment.While FIG. 7 illustrates various components of a computer system, it isnot intended to represent any particular architecture or manner ofinterconnecting the components. One embodiment may use other systemsthat have fewer or more components than those shown in FIG. 7.

In FIG. 7, the data processing system (170) includes an inter-connect(171) (e.g., bus and system core logic), which interconnects amicroprocessor(s) (173) and memory (167). The microprocessor (173) iscoupled to cache memory (179) in the example of FIG. 7.

In one embodiment, the inter-connect (171) interconnects themicroprocessor(s) (173) add the memory (167) together and alsointerconnects them to input/output (I/O) device(s) (175) via I/Ocontroller(s) (177). I/O devices (175) may include a display deviceand/or peripheral devices, such as mice, keyboards, modems, networkinterfaces, printers, scanners, video cameras and other devices known inthe art. In one embodiment, when the data processing system is a serversystem, some of the I/O devices (175), such as printers, scanners, mice,and/or keyboards, are optional.

In one embodiment, the inter-connect (171) includes one or more busesconnected to one another through various bridges, controllers and/oradapters. In one embodiment the I/O controllers (177) include a USB(Universal Serial Bus) adapter for controlling USB peripherals, and/oran IEEE-1394 bus adapter for controlling IEEE-1394 peripherals.

In one embodiment, the memory (167) includes one or more of: ROM (ReadOnly Memory), volatile RAM (Random Access Memory), and non-volatilememory, such as hard drive, flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In this description, some functions and operations are described asbeing performed by or caused by software code to simplify description.However, such expressions are also used to specify that the functionsresult from execution of the code/instructions by a processor, such as amicroprocessor.

Alternatively, or in combination, the functions and operations asdescribed here can be implemented using special purpose circuitry, withor without software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

While one embodiment can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited torecordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROMS), Digital Versatile Disks (DVDs), etc.), amongothers. The computer-readable media may store the instructions.

The instructions may also be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, etc. However, propagated signals, such as carrier waves,infrared signals, digital signals, etc. are not tangible machinereadable medium and are not configured to store instructions.

In general, a machine readable medium includes any mechanism thatprovides (i.e., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

Other Aspects

The description and drawings are illustrative and are not to beconstrued as limiting. The present disclosure is illustrative ofinventive features to enable a person skilled in the art to make and usethe techniques. Various features, as described herein, should be used incompliance with all current and future rules, laws and regulationsrelated to privacy, security, permission, consent, authorization, andothers. Numerous specific details are described to provide a thoroughunderstanding. However, in certain instances, well known or conventionaldetails are not described in order to avoid obscuring the description.References to one or an embodiment in the present disclosure are notnecessarily references to the same embodiment; and, such references meanat least one.

The use of headings herein is merely provided for ease of reference, andshall not be interpreted in any way to limit this disclosure or thefollowing claims.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,and are not necessarily all referring to separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by one embodiment and notby others. Similarly, various requirements are described which may berequirements for one embodiment but not other embodiments. Unlessexcluded by explicit description and/or apparent incompatibility, anycombination of various features described in this description is alsoincluded here.

The disclosures of the above discussed patent documents are herebyincorporated herein by reference.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method, comprising:storing, by a computing apparatus, transaction data recordingtransactions processed by a transaction handler; organizing, by acomputing apparatus, third party data according to community, whereinthe third party data includes first data received from a first pluralityof entities of a first community and second data received from a secondplurality of entities of a second community; and responsive to a requestfrom a merchant in the second community, presenting, by the computingapparatus, an offer of the merchant in the second community to usersidentified via the transaction data and the first data received from thefirst plurality of entities of the first community.
 2. The method ofclaim 1, wherein the merchant in the second community is not in thefirst community.
 3. The method of claim 2, wherein the first datacomprises limitations on access to the first data by entities outsidethe first community; and the users are identified in accordance with thelimitations.
 4. The method of claim 3, wherein the request specifiesthat the users be identified as customers of a merchant in the firstcommunity who have not made purchases from the merchant in the secondcommunity in a recent time period.
 5. The method of claim 3, wherein thelimitations are specified by a merchant, an issuer, or an acquirer inthe first community.
 6. The method of claim 5, further comprising:monitoring first transactions of the users during processing of thefirst transactions by the transaction handler; detecting a subset of thefirst transactions satisfying presentation requirements of the offer;and presenting the offer of the merchant in the second community tousers corresponding to the subset of the first transactions in real-timewith processing respective transactions in the subset by the transactionhandler.
 7. The method of claim 6, wherein the request specifies thatthe first transactions with one or more merchants in the first communitybe monitored for the presenting of the offer.
 8. The method of claim 7,wherein the request specifies that users enrolled in the first communitybe monitored for the presenting of the offer.
 9. The method of claim 8,wherein the offer of the merchant in the second community is transmittedto the users that are identified via the transaction data and the firstdata via communication references registered during enrollment in thefirst community.
 10. The method of claim 9, wherein the communicationreferences include at least one of: phone number and email address. 11.The method of claim 10, wherein the first data includes thecommunication references.
 12. The method of claim 1, further comprising:determining a spending pattern from the transaction data, wherein theusers are identified further based on the spending pattern.
 13. Themethod of claim 12, wherein the spending pattern includes a plurality oftransactions; and a last transaction in the spending pattern triggers apresentation of the offer.
 14. The method of claim 1, furthercomprising: predicting a time of a next purchase of a first useridentified via the transaction data and the first data received from thefirst plurality of entities of the first community, wherein the offer isprovided to the first user prior to the predicted time of the nextpurchase.
 15. The method of claim 14, wherein the offer provided to thefirst user is configured to expire prior to the predicted time of thenext purchase.
 16. The method of claim 1, wherein the first dataincludes identification information of account holders who have enrolledin the first community; and the users are identified based at least inpart on the identification information.
 17. A computer storage devicestoring instructions which, when executed by a computer, cause thecomputer to perform a method, comprising: storing, by the computer,transaction data recording transactions processed by a transactionhandler; organizing, by the computer, third party data according tocommunity, wherein the third party data includes first data receivedfrom a first plurality of entities of a first community and second datareceived from a second plurality of entities of a second community; andresponsive to a request from a merchant in the second community,presenting, by the computer, an offer of the merchant in the secondcommunity to users identified via the transaction data and the firstdata received from the first plurality of entities of the firstcommunity.
 18. A computing apparatus, comprising: a transaction handler;a data warehouse coupled with the transaction handler and configured tostore transaction data recording transactions processed by thetransaction handler, each of the transactions being processed inresponse to an account identifier being submitted from an acquirerprocessor for a payment from an issuer, the account identifier beingissued by the issuer to a user of the account identifier, an issuerprocessor to make the payment on behalf of the user, the acquirerprocessor to receive the payment on behalf of a merchant from which theuser makes a purchase; a portal coupled with the data warehouse andconfigured to receive first data from a first plurality of entities of afirst community and second data from a second plurality of entities of asecond community, the portal further configured to receive a requestfrom a merchant in the second community for an offer; and a messagebroker coupled with the data warehouse to generate a message containingthe offer to users identified via the transaction data and the firstdata received from the first plurality of entities of the firstcommunity.
 19. The computing apparatus of claim 18, further comprising:an event detection engine coupled with the message broker and the datawarehouse to detect, based on the transaction data, an event satisfyingrequirements for presentation of the offer.
 20. The computing apparatusof claim 19, further comprising: a pattern recognition engine coupledwith the data warehouse and the event detection engine to detectspending patterns, wherein at least a portion of the requirements forthe presentation of the offer is based on detection of the spendingpatterns.